From d0a662f068842561557b1298f10e1e65681ce62c Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Wed, 29 Mar 2023 17:54:17 -0700 Subject: [PATCH 01/33] initial commit --- README.md | 99 +- _action_files/settings.ini | 2 +- _config.yml | 4 +- _fastpages_docs/_setup_pr_template.md | 10 +- notebooks/kdd_intro_to_EconML.ipynb | 6936 ++++++++++++------------- 5 files changed, 3507 insertions(+), 3544 deletions(-) diff --git a/README.md b/README.md index 4af745a..7eb7a77 100755 --- a/README.md +++ b/README.md @@ -1,32 +1,31 @@ [//]: # (This template replaces README.md when someone creates a new repo with the fastpages template.) -![](https://github.com/causal-machine-learning/kdd2021-tutorial/workflows/CI/badge.svg) -![](https://github.com/causal-machine-learning/kdd2021-tutorial/workflows/GH-Pages%20Status/badge.svg) +![](https://github.com/causal-machine-learning/kdd2023-workshop/workflows/CI/badge.svg) +![](https://github.com/causal-machine-learning/kdd2023-workshop/workflows/GH-Pages%20Status/badge.svg) [![](https://img.shields.io/static/v1?label=fastai&message=fastpages&color=57aeac&labelColor=black&style=flat&logo=data:image/png;base64,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)](https://github.com/fastai/fastpages) -# **Causal Inference and Machine Learning in Practice with EconML and CausalML**: Industrial Use Cases at Microsoft, TripAdvisor, Uber +# **Causal Inference and Machine Learning in Practice**: Use cases for Product, Brand, Policy and Beyond ## **Schedule** -### Time - -* 4:00 AM - 7:00 AM August 15, 2021 [SGT](https://www.timeanddate.com/worldclock/converter.html?iso=20210814T200000&p1=236&p2=tz_pt&p3=tz_et) -* 4:00 PM - 7:00 PM August 14, 2021 [EDT](https://www.timeanddate.com/worldclock/converter.html?iso=20210814T200000&p1=236&p2=tz_pt&p3=tz_et) -* 1:00 PM - 4:00 PM August 14, 2021 [PDT](https://www.timeanddate.com/worldclock/converter.html?iso=20210814T200000&p1=236&p2=tz_pt&p3=tz_et) - -### Live Zoom Link - -To be shared within the KDD 21 Virtual Platform during the conference. +* Long Beach Convention & Entertainment Center, 300 E Ocean Blvd, Long Beach, CA 90802 ([Google Map](https://goo.gl/maps/1N3XGEovGgJqXAV98)) +* 9:00 AM - 1:00 PM August 7, 2023 [PDT] ## **Abstract** In recent years, both academic research and industry applications see an increased effort in using machine learning methods to measure granular causal effects and design optimal policies based on these causal estimates. Open source packages such as [CausalML](https://github.com/uber/causalml) and [EconML](https://github.com/microsoft/econml) provide a unified interface for applied researchers and industry practitioners with a variety of machine learning methods for causal inference. The tutorial will cover the topics including conditional treatment effect estimators by meta-learners and tree-based algorithms, model validations and sensitivity analysis, optimization algorithms including policy leaner and cost optimization. In addition, the tutorial will demonstrate the production of these algorithms in industry use cases. -## **Target Audience and Prerequisites for the Tutorial** +## **Target Audience for the Workshop** Anyone who is interested in causal inference and machine learning, especially economists/statisticians/data scientists who want to learn how to combine causal inference and machine learning with real industry use cases incorporated in large scaled machine learning systems at companies such as Microsoft, TripAdvisor and Uber. The tutorial assumes some basic knowledge in statistical methods, machine learning algorithms and the Python programming language. +## **Important Dates** + +* May 23, 2023 [AoE]: Workshop paper submission deadline +* June 23, 2023: Paper decision notifications +* August 7, 2023: Workshop + ## **Outline** | **Title** | **Duration** | Slides | Code | @@ -41,80 +40,44 @@ The tutorial assumes some basic knowledge in statistical methods, machine learni | Case Study #3: Customer Segmentation at TripAdvisor with Recommendation A/B Tests | 30 minutes | [Slides](https://drive.google.com/file/d/1yyIu_3epIVXbwzJj658Iv4vxHGjtPh8n/view?usp=sharing) | [Notebook](https://colab.research.google.com/drive/1nUhkLVpanv-gm_oA7FbValhpDpEs02wR#scrollTo=qk4_f4tx5gZz) | | Case Study #4: Long-Term Return-on-Investment at Microsoft via Short-Term Proxies | 30 minutes | [Slides](https://drive.google.com/file/d/1FEKXFHHATntHjsEymXnEw6GAiUGMm8sG/view?usp=sharing) | [Notebook](https://colab.research.google.com/drive/1Ow7ArXRn1NJq47OLvchi26RRTdm94yv8?usp=sharing) | -## **Presentation Abstracts** +## **Keynote Speakers** -### Introduction to Causal Inference +### Yoda We will give an overview of basic concepts in causal inference. A quick refresher on the main tools and terminology of causal inference: correlation vs causation, average, conditional, and individual treatment effects, causal inference via randomization, Causal inference using instrumental variables, Causal inference via unconfoundedness. -### Introduction to CasualML +### Darth Vader We will provide an overview of CausalML, an open source Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research. We will introduce the main components of CausalML: (1) inference with causal machine learning algorithms (e.g. meta-learners, uplift trees, CEVAE, dragonnet), (2) validation/analysis methods (e.g. synthetic data generation, AUUC, sensitivity analysis, interpretability), (3) optimization methods (e.g. policy optimization, value optimization, unit selection). -### Case #1: Causal Impact Analysis with Observational Data at Uber +### Luke Skywalker As an introductory case study for using causal inference, we will cover the use case of understanding the causal impact from observational data in the context of cross sell at Uber. We emphasize that simple comparisons of users who make cross purchase or not will produce biased estimates and that can be demonstrated in the causal inference framework. We show the use of different causal estimation methodologies through propensity score matching and meta learners to estimate the causal impact. In addition, we will use sensitivity analysis to show the robustness of the estimates. -### Case #2: Targeting Optimization: Bidder at Uber +### Princess Leia We will introduce the audience selection method with uplift modeling in online RTB, which aims to estimate heterogeneous treatment effects for advertising. It has been studied to provide a superior return on investment by selecting the most incremental users for a specific campaign. To examine the effectiveness of uplift modeling in the context of real-time bidding, we conducted the comparative analysis of four different meta-learners on real campaign data. We adapted an explore-exploit set up for offline training and online evaluation. We will also introduce how we use Targeted Maximum Likelihood Estimation (TMLE) based Average Treatment Effect (ATE) as ground truth for evaluation. -### Introduction to EconML - -We will provide an overview of recent methodologies that combine machine learning with causal inference and the significant statistical power that machine learning brings to causal inference estimation methods. We will outline the structure and capabilities of the EconML package and describe some of the key causal machine learning methodologies that are implemented (e.g. double machine learning, causal forests, deepiv, doubly robust learning, dynamic double machine learning). We will also outline approaches to confidence interval construction (e.g. bootstrap, bootstrap-of-little-bags, debiased lasso), interpretability (shap values, tree interpreters) and policy learning (doubly robust policy learning). - -### Case #3: Customer Segmentation at TripAdvisor with Recommendation A/B Tests +## **Accepted Papers** -We examine the scenario in which we wish to learn heterogeneous treatment effects (CATE), but observational data is biased and direct experimental data (e.g. A/B test) is plagued by imperfect compliance. In this setup, TripAdvisor would like to know whether joining a membership program compels users to spend more time engaging with the website and purchasing more products. The usual approach, a direct A/B test, is infeasible: the website cannot force users to comply and become members, hence the imperfect compliance that can bias calculations. The solution is to use an alternative A/B test that was originally designed to measure whether an easier sign-up process would promote user membership. This A/B test plays the role of an instrument that nudges users to sign up for membership. We introduce EconML’s IntentToTreatDRIV estimator which can leverage this repurposed A/B test to both learn the effect of membership on user engagement and understand how these effects vary with customer features. We show how this novel methodology led to extracting key business insights and helped TripAdvisor understand and differentiate how customers engage with their platform. +## **Organizers** -### Case #4: Long-Term Return-on-Investment at Microsoft via Short-Term Proxies +* Chu Wang, Amazon +* Yingfei Wang, University of Washington +* Xinwei Ma, UC San Diego +* [Zeyu Zheng](mailto:zyzheng@berkeley.edu), UC Berkeley, Amazon - main contact -In this case study, we talk about using observational data to measure the long term Return-on-Investment of some types of dollar value investments Microsoft gives to the enterprise customers. There are many challenges for this setting, for instance, we don't have enough period of data to identify a long term ROI, we should control the effect coming from the future investment and we are in a high dimensional data space. We then propose a surrogate based approach assuming the long-term effect is channeled through some short-term proxies and employ a dynamic adjustment to the surrogate model in order to get rid of the effect from future investment, finally apply double machine learning (DML) techniques to estimate the ROI. We apply this methodology to answer the questions like what is the average long-run ROI on each type of the investment? What types of customers have a higher ROI to a specific investment? And how different incentives impact the different solution areas. Finally we will showcase how you could use EconML to solve similar problems by only a few lines of code. +### CausalML Team - -## **Tutors** - -### Presenters - -* Jing Pan, Uber, CausalML +* Jing Pan, Snap, CausalML * Yifeng Wu, Uber, CausalML -* Huigang Chen, Facebook, CausalML -* Totte Harinen, Toyota Research Institute, CausalML -* Paul Lo, Uber, CausalML -* Greg Lewis, Microsoft Research, EconML -* Vasilis Syrgkanis, Microsoft Research, EconML -* Miruna Oprescu, Microsoft Research, EconML -* Maggie Hei, Microsoft Research, EconML +* Huigang Chen, Meta, CausalML +* Totte Harinen, AirBnB, CausalML +* Paul Lo, Snap, CausalML +* [Jeong-Yoon Lee](mailto:jeong@uber.com), Uber, CausalML - main contact +* Zhenyu Zhao, Tencent, CausalML -### Contributors +### EconML Team -* Jeong-Yoon Lee, Netflix Research, CausalML -* Zhenyu Zhao, Tencent, CausalML * Keith Battocchi, Microsoft Research, EconML * Eleanor Dillon, Microsoft Research, EconML - - -## **References** - -1. Künzel, Sören R., et al. "Metalearners for estimating heterogeneous treatment effects using machine learning." Proceedings of the national academy of sciences 116.10 (2019): 4156-4165. ([paper](https://www.pnas.org/content/pnas/116/10/4156.full.pdf)) -2. Chernozhukov, Victor, et al. "Double/debiased/neyman machine learning of treatment effects." American Economic Review 107.5 (2017): 261-65. ([paper](https://arxiv.org/pdf/1701.08687)) -3. Nie, Xinkun, and Stefan Wager. "Quasi-oracle estimation of heterogeneous treatment effects." arXiv preprint arXiv:1712.04912 (2017) ([paper](https://arxiv.org/pdf/1712.04912)) -4. Tso, Fung Po, et al. "DragonNet: a robust mobile internet service system for long-distance trains." IEEE transactions on mobile computing 12.11 (2013): 2206-2218. ([paper](https://eprints.gla.ac.uk/56409/1/56409.pdf)) -5. Louizos, Christos, et al. "Causal effect inference with deep latent-variable models." arXiv preprint arXiv:1705.08821 (2017) ([paper](https://arxiv.org/pdf/1705.08821)) -6. Wager, Stefan, and Susan Athey. "Estimation and inference of heterogeneous treatment effects using random forests." Journal of the American Statistical Association 113.523 (2018): 1228-1242. ([paper](https://www.tandfonline.com/doi/pdf/10.1080/01621459.2017.1319839)) -7. Oprescu, Miruna, et al. "EconML: A Machine Learning Library for Estimating Heterogeneous Treatment Effects." ([repo](https://github.com/microsoft/EconML)) -8. Chen, Huigang, et al. "Causalml: Python package for causal machine learning." arXiv preprint arXiv:2002.11631 (2020) ([repo](https://github.com/uber/causalml)) -9. Yao, Liuyi, et al. "A survey on causal inference." arXiv preprint arXiv:2002.02770 (2020). ([paper](https://arxiv.org/pdf/2002.02770.pdf)) -10. Goldenberg, Dmitri, et al. "Personalization in Practice: Methods and Applications." Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 2021 ([paper](https://drive.google.com/drive/folders/1c_khoTDRbkoRY5OiaxEfUxRQkyNv3FeK)) -11. Blackwell, Matthew. "A selection bias approach to sensitivity analysis for causal effects." Political Analysis 22.2 (2014): 169-182. ([paper](https://www.cambridge.org/core/journals/political-analysis/article/selection-bias-approach-to-sensitivity-analysis-for-causal-effects/788C169FAF5482452566811136D4F9B4)) -12. Athey, Susan, and Stefan Wager. "Efficient policy learning." arXiv preprint arXiv:1702.02896 (2017). ([paper](https://arxiv.org/pdf/1702.02896.pdf)) -13. Sharma, Amit, and Emre Kiciman. "Causal Inference and Counterfactual Reasoning." Proceedings of the 7th ACM IKDD CoDS and 25th COMAD. 2020. 369-370. ([paper](https://dl.acm.org/doi/abs/10.1145/3371158.3371231)) -14. Li, Ang, and Judea Pearl. "Unit selection based on counterfactual logic." Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. 2019 ([paper](https://par.nsf.gov/biblio/10180278)) -15. Kennedy, Edward H. "Optimal doubly robust estimation of heterogeneous causal effects." arXiv preprint arXiv:2004.14497 (2020) ([paper](https://arxiv.org/pdf/2004.14497.pdf)) -16. Gruber, Susan, and Mark J. Van Der Laan. "Targeted maximum likelihood estimation: A gentle introduction." (2009) ([paper](https://biostats.bepress.com/cgi/viewcontent.cgi?article=1255&context=ucbbiostat)) -17. D. Foster, V. Syrgkanis. Orthogonal Statistical Learning. Proceedings of the 32nd Annual Conference on Learning Theory (COLT), 2019 ([paper](https://arxiv.org/pdf/1901.09036.pdf)) -18. V. Syrgkanis, V. Lei, M. Oprescu, M. Hei, K. Battocchi, G. Lewis. Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments. Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS), 2019 ([paper](https://arxiv.org/pdf/1905.10176.pdf)) -19. M. Oprescu, V. Syrgkanis and Z. S. Wu. Orthogonal Random Forest for Causal Inference. Proceedings of the 36th International Conference on Machine Learning (ICML), 2019 ([paper](http://proceedings.mlr.press/v97/oprescu19a/oprescu19a.pdf)) -20. Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. Deep IV: A flexible approach for counterfactual prediction. Proceedings of the 34th International Conference on Machine Learning, ICML'17, 2017 ([paper](http://proceedings.mlr.press/v70/hartford17a/hartford17a.pdf)) -21. Battocchi, K., Dillon, E., Hei, M., Lewis, G., Oprescu, M., & Syrgkanis, V. (2021). Estimating the Long-Term Effects of Novel Treatments. arXiv preprint arXiv:2103.08390. ([paper](https://arxiv.org/pdf/2103.08390.pdf)) -22. Lewis, G., & Syrgkanis, V. (2020). Double/Debiased Machine Learning for Dynamic Treatment Effects. arXiv preprint arXiv:2002.07285. ([paper](https://arxiv.org/pdf/2002.07285.pdf)) diff --git a/_action_files/settings.ini b/_action_files/settings.ini index d803172..8becb3a 100644 --- a/_action_files/settings.ini +++ b/_action_files/settings.ini @@ -7,7 +7,7 @@ description = Writing a library entirely in notebooks keywords = jupyter notebook author = Sylvain Gugger and Jeremy Howard author_email = info@fast.ai -baseurl = /kdd2021-tutorial +baseurl = /kdd2023-workshop title = nbdev copyright = fast.ai license = apache2 diff --git a/_config.yml b/_config.yml index 6995623..19a39b6 100644 --- a/_config.yml +++ b/_config.yml @@ -10,7 +10,7 @@ title: EconML/CausalML KDD 2021 Tutorial description: EconML/CausalML KDD 2021 Tutorial github_username: causal-machine-learning # you can comment the below line out if your repo name is not different than your baseurl -github_repo: "kdd2021-tutorial" +github_repo: "kdd2023-workshop" # OPTIONAL: override baseurl and url if using a custom domain # Note: leave out the trailing / from this value. @@ -34,7 +34,7 @@ url: "https://causal-machine-learning.github.io" # the base hostname & protocol # # 3. You must replace the parameter `baseurl` in _action_files/settings.ini with the same value as you set here but WITHOUT QUOTES. # -baseurl: "/kdd2021-tutorial" # the subpath of your site, e.g. "/blog". +baseurl: "/kdd2023-workshop" # the subpath of your site, e.g. "/blog". # Github and twitter are optional: minima: diff --git a/_fastpages_docs/_setup_pr_template.md b/_fastpages_docs/_setup_pr_template.md index 894e80a..2bb5d67 100644 --- a/_fastpages_docs/_setup_pr_template.md +++ b/_fastpages_docs/_setup_pr_template.md @@ -4,22 +4,22 @@ Hello :wave: @causal-machine-learning! Thank you for using fastpages! 1. Create an ssh key-pair. Open this utility. Select: `RSA` and `4096` and leave `Passphrase` blank. Click the blue button `Generate-SSH-Keys`. -2. Navigate to this link and click `New repository secret`. Copy and paste the **Private Key** into the `Value` field. This includes the "---BEGIN RSA PRIVATE KEY---" and "--END RSA PRIVATE KEY---" portions. **In the `Name` field, name the secret `SSH_DEPLOY_KEY`.** +2. Navigate to this link and click `New repository secret`. Copy and paste the **Private Key** into the `Value` field. This includes the "---BEGIN RSA PRIVATE KEY---" and "--END RSA PRIVATE KEY---" portions. **In the `Name` field, name the secret `SSH_DEPLOY_KEY`.** -3. Navigate to this link and click the `Add deploy key` button. Paste your **Public Key** from step 1 into the `Key` box. In the `Title`, name the key anything you want, for example `fastpages-key`. Finally, **make sure you click the checkbox next to `Allow write access`** (pictured below), and click `Add key` to save the key. +3. Navigate to this link and click the `Add deploy key` button. Paste your **Public Key** from step 1 into the `Key` box. In the `Title`, name the key anything you want, for example `fastpages-key`. Finally, **make sure you click the checkbox next to `Allow write access`** (pictured below), and click `Add key` to save the key. ![](https://raw.githubusercontent.com/fastai/fastpages/master/_fastpages_docs/_checkbox.png) ### What to Expect After Merging This PR -- GitHub Actions will build your site, which will take 2-3 minutes to complete. **This will happen anytime you push changes to the master branch of your repository.** You can monitor the logs of this if you like on the [Actions tab of your repo](https://github.com/causal-machine-learning/kdd2021-tutorial/actions). +- GitHub Actions will build your site, which will take 2-3 minutes to complete. **This will happen anytime you push changes to the master branch of your repository.** You can monitor the logs of this if you like on the [Actions tab of your repo](https://github.com/causal-machine-learning/kdd2023-workshop/actions). - Your GH-Pages Status badge on your README will eventually appear and be green, indicating your first successful build. -- You can monitor the status of your site in the GitHub Pages section of your [repository settings](https://github.com/causal-machine-learning/kdd2021-tutorial/settings). +- You can monitor the status of your site in the GitHub Pages section of your [repository settings](https://github.com/causal-machine-learning/kdd2023-workshop/settings). If you are not using a custom domain, your website will appear at: -#### https://causal-machine-learning.github.io/kdd2021-tutorial +#### https://causal-machine-learning.github.io/kdd2023-workshop ## Optional: Using a Custom Domain diff --git a/notebooks/kdd_intro_to_EconML.ipynb b/notebooks/kdd_intro_to_EconML.ipynb index 91746dd..defca44 100644 --- a/notebooks/kdd_intro_to_EconML.ipynb +++ b/notebooks/kdd_intro_to_EconML.ipynb @@ -1,3473 +1,3473 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - }, - "colab": { - "name": "kdd_intro_to_EconML.ipynb", - "provenance": [], - "collapsed_sections": [], - "include_colab_link": true - } - 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"![EconML-Logo-MSFT-color.png](data:image/png;base64,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)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jYAHRpIF-BRW" - }, - "source": [ - "# **KDD2021 Tutorial:** [Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber](https://causal-machine-learning.github.io/kdd2021-tutorial/)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AYlHqLQf-PjP" - }, - "source": [ - "# Introduction to [EconML](https://github.com/microsoft/EconML)\n", - "\n", - "A python library for estimation of heterogeneous treatment effects with Machine Learning.\n", - "\n", - "**Presentation:** [Introduction to EconML](https://drive.google.com/file/d/1gt4KNznrYbwdryi9jGcC0-hDCNg7mBNE/view?usp=sharing)\n", - "\n", - "**Github:** https://github.com/microsoft/EconML\n", - "\n", - "**Documentation:** https://econml.azurewebsites.net/\n", - "\n", - "By the Microsoft Research project [ALICE (Automated Learning and Intelligence for Causation and Economics)](https://www.microsoft.com/en-us/research/project/alice/)" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "cEqRBSJV9dTX" - }, - "source": [ - "#!pip install econml" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "yPw9zWWm3H9e" - }, - "source": [ - "!pip install git+https://github.com/microsoft/EconML.git@mehei/driv#egg=econml" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "PKxsq0Mv9cGD" - }, - "source": [ - "import numpy as np\n", - "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", - "from sklearn.linear_model import LassoCV, Lasso\n", - "from sklearn.preprocessing import PolynomialFeatures\n", - "from sklearn.model_selection import train_test_split\n", - "import matplotlib.pyplot as plt\n", - "import scipy\n", - "import warnings\n", - "warnings.simplefilter('ignore')" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "WNvGZiKF9cGG" - }, - "source": [ - "def gen_data(n, discrete=False):\n", - " X = np.random.normal(0, 1, size=(n, 2))\n", - " W = np.random.normal(0, 1, size=(n, 2))\n", - " if discrete:\n", - " T = np.random.binomial(1, scipy.special.expit(W[:, 0]))\n", - " else:\n", - " T = W[:, 0] + np.random.normal(0, 1, size=(n,))\n", - " y = (X[:, 0] + 1) * T + W[:, 0] + np.random.normal(0, 1, size=(n,))\n", - " return y, T, X, W\n", - "\n", - "def gen_data_iv(n):\n", - " X = np.random.normal(0, 1, size=(n, 2))\n", - " W = np.random.normal(0, 1, size=(n, 2))\n", - " U = np.random.normal(0, 1, size=(n,))\n", - " Z = np.random.normal(0, 1, size=(n,))\n", - " T = Z + W[:, 0] + U\n", - " y = (X[:, 0] + 1) * T + W[:, 0] + U\n", - " return y, T, Z, X, W" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7xzAqDMb9cGH" - }, - "source": [ - "# 1. Estimation under Exogeneity" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "TuRauvKA9cGH" - }, - "source": [ - "y, T, X, W = gen_data(1000)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nHhjI8Kb9cGI", - "outputId": "059fd233-bd8e-4de3-fa61-1a042b381aba" - }, - "source": [ - "from econml.dml import NonParamDML\n", - "\n", - "est = NonParamDML(model_y=RandomForestRegressor(), # Any ML model for E[Y|X,W]\n", - " model_t=RandomForestRegressor(), # Any ML model for E[T|X,W]\n", - " model_final=RandomForestRegressor(max_depth=2), # Any ML model for CATE\n", - " discrete_treatment=False, # categorical or continuous treatment\n", - " cv=2, # number of crossfit folds\n", - " mc_iters=1) # repetitions of cross-fitting for stability\n", - "\n", - "est.fit(y, T, X=X, W=W, cache_values=True) # fit the CATE model" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 6 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Gc4fpcMr9cGJ" - }, - "source": [ - "#### Personalized effect estimates on test samples" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9rqqLxnt9cGJ", - "outputId": "cd71c483-e9e1-48f8-fab7-e8534777a008" - }, - "source": [ - "# personalized effect for each sample from going from treatment 0 to treatment level 1\n", - "est.effect(X[:5], T0=0, T1=1)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([ 1.54592291, 0.87946172, -0.47128723, 0.81285758, 3.1442986 ])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 7 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qC23J2R09cGJ" - }, - "source": [ - "#### ML model diagnostics" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "yE2v-YzX9cGK", - "outputId": "6d074083-9a40-4222-d7bb-f2d158fc2df0" - }, - "source": [ - "# fitted nuisance models for each cross-fitting fold and out-of-sample scores\n", - "est.models_y, est.nuisance_scores_y" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "([[RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False),\n", - " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False)]],\n", - " [[0.5409335662361056, 0.48599679070328405]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 8 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JXOd9Rfa9cGK", - "outputId": "dfbebf0a-19e9-4f15-9182-e22318015360" - }, - "source": [ - "est.models_t, est.nuisance_scores_t" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "([[RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False),\n", - " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False)]],\n", - " [[0.4282760371961908, 0.3782367983417111]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 9 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CLbqvUvr9cGK" - }, - "source": [ - "#### CATE model diagnostics" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IdiMMpsQ9cGL", - "outputId": "6b6277ca-32b3-4d29-f5f4-37b765551e39" - }, - "source": [ - "# in-sample goodness-of-fit score for the final cate model\n", - "print(est.score_)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "1.4717907756757855\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "968ktBK_9cGL" - }, - "source": [ - "#### Nuisance quantity diagnostics" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "KDwBlm-i9cGL" - }, - "source": [ - "# calculated residuals for each training sample\n", - "yres, Tres, X_cache, W_cache = est.residuals_" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Jkc1WSi19cGL" - }, - "source": [ - "# 2. Estimation with Instruments" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "8pjyjlRQ9cGM" - }, - "source": [ - "y, T, Z, X, W = gen_data_iv(2000)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "l5gr5sGS9cGM" - }, - "source": [ - "from econml.iv.dml import OrthoIV\n", - "\n", - "est = OrthoIV(model_y_xw=RandomForestRegressor(), # ML model for E[Y|X,W]\n", - " model_t_xw=RandomForestRegressor(), # ML model for E[T|X,W]\n", - " model_z_xw=RandomForestRegressor(), # ML model for E[Z|X,W]\n", - " discrete_treatment=False, # categorical/continuous treatment\n", - " discrete_instrument=False, # categorical/continuous instrument\n", - " cv=2, # number of crossfit folds\n", - " mc_iters=1) # repetitions of cross-fitting for stability" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "b_k1NpEp9cGM", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "8b057fc5-bd87-43fa-ea75-33ee736a2a88" - }, - "source": [ - "est.fit(y, T, Z=Z, X=X, W=W, cache_values=True)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 14 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "a0zr3m3r9cGM" - }, - "source": [ - "#### Personalized effect estimates on test samples" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "2wAnhhq99cGN", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "2061468c-6969-4b1a-86fa-ba16a8f6b49c" - }, - "source": [ - "est.effect(X, T0=0, T1=1)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([-0.32390668, 2.23728003, 0.70346148, ..., 0.85984079,\n", - " 0.17539819, 1.07325619])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 15 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "D9ESfbo29cGN" - }, - "source": [ - "#### ML model diagnostics" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "9JlbW4an9cGN", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "f77e36a8-295f-4dff-e11d-21057357e4c8" - }, - "source": [ - "est.models_y_xw, est.nuisance_scores_y_xw" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "([[RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False),\n", - " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False)]],\n", - " [[0.3493220983540998, 0.2621905464991312]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 16 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "e1YCQWZK9cGO", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "71292840-3f77-4985-fdf3-855ad7d4f07a" - }, - "source": [ - "est.models_t_xw, est.nuisance_scores_t_xw" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "([[RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False),\n", - " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False)]],\n", - " [[0.24243204274642238, 0.22952337252664082]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 17 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "cN5Ga_YH9cGO", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "fca11455-0e2c-4881-96a9-0e07a67d4461" - }, - "source": [ - "est.models_z_xw, est.nuisance_scores_z_xw" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "([[RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False),\n", - " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False)]],\n", - " [[-0.10057707531258632, -0.08476759034439452]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 18 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oNkC5W9i9cGP" - }, - "source": [ - "#### CATE model diagnostics" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "zENiyXuV9cGP", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "621aba3d-e433-4c89-e0dc-1879afd565f6" - }, - "source": [ - "print(est.score_)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "5.018208071305707e-16\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "slJuNhWU9cGP" - }, - "source": [ - "#### Nuisance quantity diagnostics" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "1o5aP05y9cGP" - }, - "source": [ - "yres, Tres, Zres, Xc, Wc, Zc = est.residuals_" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WrsMl9pm9cGQ" - }, - "source": [ - "# 3. Inference" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "oeTIUL6_9cGQ" - }, - "source": [ - "y, T, X, W = gen_data(1000)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "K0kinaK__Djv" - }, - "source": [ - "### Generic Bootstrap Inference" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "rSGauZ1k9cGQ" - }, - "source": [ - "from econml.dml import NonParamDML\n", - "from econml.sklearn_extensions.linear_model import WeightedLasso\n", - "\n", - "est = NonParamDML(model_y=Lasso(alpha=.1), # Any ML model for E[Y|X,W]\n", - " model_t=Lasso(alpha=.1), # Any ML model for E[T|X,W]\n", - " model_final=WeightedLasso(alpha=.1), # Any ML model for CATE that accepts `sample_weight` at fit\n", - " discrete_treatment=False, # categorical or continuous treatment\n", - " cv=2, # number of crossfit folds\n", - " mc_iters=1) # repetitions of cross-fitting for stability" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "lckeXBSV9cGR", - "outputId": "d1cecd80-0022-4e16-c4a9-dc1081f474f3" - }, - "source": [ - "est.fit(y, T, X=X, W=W, inference='bootstrap') # fit the CATE model" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 23 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 235 - }, - "id": "p-RP37i-9cGR", - "outputId": "2126735a-91d6-4908-eb8a-b36bcae1cbc8" - }, - "source": [ - "est.effect_inference(X[:5], T0=0, T1=1).summary_frame()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
01.2700.03932.6200.01.2101.334
11.7070.05133.6340.01.6311.787
22.4170.07930.4280.02.3042.559
31.3740.04133.7000.01.3121.445
4-0.3020.070-4.3380.0-0.449-0.216
\n", - "
" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "0 1.270 0.039 32.620 0.0 1.210 1.334\n", - "1 1.707 0.051 33.634 0.0 1.631 1.787\n", - "2 2.417 0.079 30.428 0.0 2.304 2.559\n", - "3 1.374 0.041 33.700 0.0 1.312 1.445\n", - "4 -0.302 0.070 -4.338 0.0 -0.449 -0.216" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 24 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 251 - }, - "id": "9tGAONKF9cGS", - "outputId": "cce9a4d0-d2a9-4991-87aa-90504695a8a9" - }, - "source": [ - "est.ate_inference(X)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Uncertainty of Mean Point Estimate
mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper
0.989 0.061 16.1 0.0 0.888 1.09
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Distribution of Point Estimate
std_point pct_point_lower pct_point_upper
1.041 -0.704 2.734
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Total Variance of Point Estimate
stderr_point ci_point_lower ci_point_upper
1.042 -0.698 2.737


Note: The stderr_mean is a conservative upper bound." - ], - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 25 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ab22GTTm9cGS", - "outputId": "b26f4c08-1938-43fb-c81b-36ca177254f8" - }, - "source": [ - "from econml.inference import BootstrapInference\n", - "est.fit(y, T, X=X, W=W,\n", - " inference=BootstrapInference(n_bootstrap_samples=100,\n", - " bootstrap_type='normal'))" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 26 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 235 - }, - "id": "MK02FTmf9cGS", - "outputId": "3d1bb9f4-cbcf-45ac-930b-f6d837b407b0" - }, - "source": [ - "est.effect_inference(X[:5], T0=0, T1=1).summary_frame()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
01.2670.04627.5910.01.1921.343
11.7040.05530.9780.01.6131.794
22.4140.07930.6230.02.2842.543
31.3720.04828.8790.01.2941.450
4-0.3030.073-4.1250.0-0.424-0.182
\n", - "
" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "0 1.267 0.046 27.591 0.0 1.192 1.343\n", - "1 1.704 0.055 30.978 0.0 1.613 1.794\n", - "2 2.414 0.079 30.623 0.0 2.284 2.543\n", - "3 1.372 0.048 28.879 0.0 1.294 1.450\n", - "4 -0.303 0.073 -4.125 0.0 -0.424 -0.182" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 27 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 251 - }, - "id": "qIKkO36S9cGT", - "outputId": "574a2f94-2cca-463f-adf6-b2193d1d0e6c" - }, - "source": [ - "est.ate_inference(X)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Uncertainty of Mean Point Estimate
mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper
0.986 0.065 15.226 0.0 0.88 1.093
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Distribution of Point Estimate
std_point pct_point_lower pct_point_upper
1.039 -0.704 2.73
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Total Variance of Point Estimate
stderr_point ci_point_lower ci_point_upper
1.041 -0.703 2.735


Note: The stderr_mean is a conservative upper bound." - ], - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 28 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LH0Uo_yJ--G_" - }, - "source": [ - "### Tailored Valid Inference" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZfUSZoW-4TJQ" - }, - "source": [ - "#### Heteroskedasticity-robust OLS inference for linear CATE models $\\theta(x)=\\langle\\theta, \\phi(x)\\rangle$" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "uPiPxMU19cGT" - }, - "source": [ - "from econml.dml import LinearDML\n", - "\n", - "est = LinearDML(model_y=RandomForestRegressor(), # Any ML model for E[Y|X,W]\n", - " model_t=RandomForestRegressor(), # Any ML model for E[T|X,W]\n", - " featurizer=PolynomialFeatures(degree=2, include_bias=False)) # any featurizer for " - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "qAQKnPA59cGU", - "outputId": "77583b37-6895-4c2e-c636-d948a6de8798" - }, - "source": [ - "est.fit(y, T, X=X, W=W)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 30 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 353 - }, - "id": "Cy73CEcm9cGU", - "outputId": "749cefb2-54d6-475a-d630-4a8d9beba4f6" - }, - "source": [ - "est.summary()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 1.001 0.034 29.398 0.0 0.945 1.057
X1 0.042 0.032 1.302 0.193 -0.011 0.094
X0^2 0.011 0.025 0.447 0.655 -0.029 0.052
X0 X1 0.032 0.027 1.203 0.229 -0.012 0.076
X1^2 -0.024 0.019 -1.269 0.204 -0.056 0.007
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CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 0.99 0.046 21.503 0.0 0.914 1.066


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "===========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-----------------------------------------------------------\n", - "X0 1.001 0.034 29.398 0.0 0.945 1.057\n", - "X1 0.042 0.032 1.302 0.193 -0.011 0.094\n", - "X0^2 0.011 0.025 0.447 0.655 -0.029 0.052\n", - "X0 X1 0.032 0.027 1.203 0.229 -0.012 0.076\n", - "X1^2 -0.024 0.019 -1.269 0.204 -0.056 0.007\n", - " CATE Intercept Results \n", - "====================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------------------\n", - "cate_intercept 0.99 0.046 21.503 0.0 0.914 1.066\n", - "--------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 38 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 235 - }, - "id": "og2hubAj9cGV", - "outputId": "5f0c835f-dcff-4f9c-d1be-dc6741da2a10" - }, - "source": [ - "est.effect_inference(X[:5], T0=0, T1=1).summary_frame()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
01.2610.04726.7320.01.1841.339
11.7850.06826.2740.01.6731.897
22.4160.08329.0690.02.2802.553
31.4180.04829.8280.01.3401.497
4-0.2720.064-4.2660.0-0.378-0.167
\n", - "
" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "0 1.261 0.047 26.732 0.0 1.184 1.339\n", - "1 1.785 0.068 26.274 0.0 1.673 1.897\n", - "2 2.416 0.083 29.069 0.0 2.280 2.553\n", - "3 1.418 0.048 29.828 0.0 1.340 1.497\n", - "4 -0.272 0.064 -4.266 0.0 -0.378 -0.167" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 32 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aMqhTNEb4Zt4" - }, - "source": [ - "#### Debiased Lasso Inference for high-dimensional linear CATE models $\\theta(x)=\\langle\\theta, \\phi(x)\\rangle$" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "dtjN8aC99cGY" - }, - "source": [ - "from econml.dml import SparseLinearDML\n", - "\n", - "est = SparseLinearDML(featurizer=PolynomialFeatures(degree=3, include_bias=False))" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "W3xElnFJ9cGY", - "outputId": "89e6d659-d695-49c0-927c-66e3128c3e13" - }, - "source": [ - "est.fit(y, T, X=X, W=W)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 40 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 437 - }, - "id": "6XHdjW9I9cGY", - "outputId": "b8ad3ad6-7006-47ac-c431-d90c109cf0e3" - }, - "source": [ - "est.summary()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 0.99 0.065 15.182 0.0 0.882 1.097
X1 0.012 0.07 0.179 0.858 -0.102 0.127
X0^2 0.071 0.022 3.168 0.002 0.034 0.107
X0 X1 -0.012 0.034 -0.337 0.736 -0.068 0.045
X1^2 -0.019 0.027 -0.686 0.493 -0.063 0.026
X0^3 0.004 0.013 0.328 0.743 -0.017 0.026
X0^2 X1 0.026 0.025 1.025 0.305 -0.016 0.067
X0 X1^2 0.021 0.026 0.806 0.42 -0.022 0.065
X1^3 0.001 0.016 0.054 0.957 -0.026 0.027
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CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 0.945 0.051 18.628 0.0 0.862 1.029


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "=============================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-------------------------------------------------------------\n", - "X0 0.99 0.065 15.182 0.0 0.882 1.097\n", - "X1 0.012 0.07 0.179 0.858 -0.102 0.127\n", - "X0^2 0.071 0.022 3.168 0.002 0.034 0.107\n", - "X0 X1 -0.012 0.034 -0.337 0.736 -0.068 0.045\n", - "X1^2 -0.019 0.027 -0.686 0.493 -0.063 0.026\n", - "X0^3 0.004 0.013 0.328 0.743 -0.017 0.026\n", - "X0^2 X1 0.026 0.025 1.025 0.305 -0.016 0.067\n", - "X0 X1^2 0.021 0.026 0.806 0.42 -0.022 0.065\n", - "X1^3 0.001 0.016 0.054 0.957 -0.026 0.027\n", - " CATE Intercept Results \n", - "====================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------------------\n", - "cate_intercept 0.945 0.051 18.628 0.0 0.862 1.029\n", - "--------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 41 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 235 - }, - "id": "nYXaiMBX9cGZ", - "outputId": "34e12072-ae19-40c5-d75f-72d96ff4682c" - }, - "source": [ - "est.effect_inference(X[:5], T0=0, T1=1).summary_frame()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
01.2440.06120.4580.0001.1441.344
11.7430.10217.1200.0001.5751.910
22.5280.09227.5530.0002.3772.679
31.3660.06122.5210.0001.2661.466
4-0.2360.085-2.7780.005-0.376-0.096
\n", - "
" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "0 1.244 0.061 20.458 0.000 1.144 1.344\n", - "1 1.743 0.102 17.120 0.000 1.575 1.910\n", - "2 2.528 0.092 27.553 0.000 2.377 2.679\n", - "3 1.366 0.061 22.521 0.000 1.266 1.466\n", - "4 -0.236 0.085 -2.778 0.005 -0.376 -0.096" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 42 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9kI_3o004cnQ" - }, - "source": [ - "#### Bootstrap-of-Little-Bags inference for forests CATE models $\\theta(x)$" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "JVKLLU5H9cGZ" - }, - "source": [ - "y, T, X, W = gen_data(2000, discrete=True)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "TzNqV3HW9cGZ", - "outputId": "e829ef4f-3016-431a-91d3-4864c9cca580" - }, - "source": [ - "from econml.dml import CausalForestDML\n", - "\n", - "est = CausalForestDML(discrete_treatment=True,\n", - " criterion='mse', n_estimators=1000)\n", - "est.tune(y, T, X=X, W=W)\n", - "est.fit(y, T, X=X, W=W, cache_values=True)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 44 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 451 - }, - "id": "PhogESjH9cGa", - "outputId": "97fea945-cc4e-4641-cb7b-e20f8e61064d" - }, - "source": [ - "est.summary()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Population summary of CATE predictions on Training Data\n" - ], - "name": "stdout" - }, - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Uncertainty of Mean Point Estimate
mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper
1.099 0.205 5.362 0.0 0.762 1.436
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Distribution of Point Estimate
std_point pct_point_lower pct_point_upper
1.086 -0.862 2.947
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Total Variance of Point Estimate
stderr_point ci_point_lower ci_point_upper
1.105 -0.862 2.965
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Doubly Robust ATE on Training Data Results
point_estimate stderr zstat pvalue ci_lower ci_upper
ATE 1.095 0.056 19.388 0.0 1.002 1.187
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Doubly Robust ATT(T=0) on Training Data Results
point_estimate stderr zstat pvalue ci_lower ci_upper
ATT 1.075 0.078 13.719 0.0 0.947 1.204
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Doubly Robust ATT(T=1) on Training Data Results
point_estimate stderr zstat pvalue ci_lower ci_upper
ATT 1.116 0.081 13.721 0.0 0.982 1.25


Note: The stderr_mean is a conservative upper bound." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Uncertainty of Mean Point Estimate \n", - "===============================================================\n", - "mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper\n", - "---------------------------------------------------------------\n", - " 1.099 0.205 5.362 0.0 0.762 1.436\n", - " Distribution of Point Estimate \n", - "=========================================\n", - "std_point pct_point_lower pct_point_upper\n", - "-----------------------------------------\n", - " 1.086 -0.862 2.947\n", - " Total Variance of Point Estimate \n", - "==========================================\n", - "stderr_point ci_point_lower ci_point_upper\n", - "------------------------------------------\n", - " 1.105 -0.862 2.965\n", - " Doubly Robust ATE on Training Data Results \n", - "=========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------\n", - "ATE 1.095 0.056 19.388 0.0 1.002 1.187\n", - " Doubly Robust ATT(T=0) on Training Data Results \n", - "=========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------\n", - "ATT 1.075 0.078 13.719 0.0 0.947 1.204\n", - " Doubly Robust ATT(T=1) on Training Data Results \n", - "=========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------\n", - "ATT 1.116 0.081 13.721 0.0 0.982 1.25\n", - "---------------------------------------------------------\n", - "\n", - "Note: The stderr_mean is a conservative upper bound.\n", - "\"\"\"" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 45 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 235 - }, - "id": "bZzv-0bo9cGa", - "outputId": "26e8e29b-691e-4fa4-d469-7b00ee463ac4" - }, - "source": [ - "est.effect_inference(X[:5], T0=0, T1=1).summary_frame()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
point_estimatestderrzstatpvalueci_lowerci_upper
X
01.9090.2308.2930.0001.5312.288
10.3080.1132.7350.0060.1230.493
2-0.1130.204-0.5530.580-0.4490.223
31.2770.1926.6650.0000.9621.593
42.3950.22510.6390.0002.0242.765
\n", - "
" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "0 1.909 0.230 8.293 0.000 1.531 2.288\n", - "1 0.308 0.113 2.735 0.006 0.123 0.493\n", - "2 -0.113 0.204 -0.553 0.580 -0.449 0.223\n", - "3 1.277 0.192 6.665 0.000 0.962 1.593\n", - "4 2.395 0.225 10.639 0.000 2.024 2.765" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 46 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qqO9by3p9cGa" - }, - "source": [ - "# 4. Causal Scoring" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "9HwG--DU9cGa" - }, - "source": [ - "y, T, X, W = gen_data(2000, discrete=True)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Rr5El3LR9cGa" - }, - "source": [ - "#### Multitude of approaches for CATE estimation to select from" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "je0AkjG99cGb" - }, - "source": [ - "from econml.dml import DML, LinearDML, SparseLinearDML, NonParamDML\n", - "from econml.metalearners import XLearner, TLearner, SLearner, DomainAdaptationLearner\n", - "from econml.dr import DRLearner\n", - "\n", - "reg = lambda: RandomForestRegressor(min_samples_leaf=10)\n", - "clf = lambda: RandomForestClassifier(min_samples_leaf=10)\n", - "# A multitude of possible approaches for CATE estimation under conditional exogeneity\n", - "models = [('ldml', LinearDML(model_y=reg(), model_t=clf(), discrete_treatment=True,\n", - " linear_first_stages=False, cv=3)),\n", - " ('sldml', SparseLinearDML(model_y=reg(), model_t=clf(), discrete_treatment=True,\n", - " featurizer=PolynomialFeatures(degree=2, include_bias=False),\n", - " linear_first_stages=False, cv=3)),\n", - " ('xlearner', XLearner(models=reg(), cate_models=reg(), propensity_model=clf())),\n", - " ('dalearner', DomainAdaptationLearner(models=reg(), final_models=reg(),\n", - " propensity_model=clf())),\n", - " ('slearner', SLearner(overall_model=reg())),\n", - " ('tlearner', TLearner(models=reg())),\n", - " ('drlearner', DRLearner(model_propensity=clf(), model_regression=reg(),\n", - " model_final=reg(), cv=3)),\n", - " ('rlearner', NonParamDML(model_y=reg(), model_t=clf(), model_final=reg(),\n", - " discrete_treatment=True, cv=3)),\n", - " ('dml3dlasso', DML(model_y=reg(), model_t=clf(), model_final=LassoCV(),\n", - " discrete_treatment=True,\n", - " featurizer=PolynomialFeatures(degree=3),\n", - " linear_first_stages=False, cv=3))\n", - "]" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "it8Mb8219cGb" - }, - "source": [ - "#### Split the data in train and validation" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "dsqFr4Cz9cGb" - }, - "source": [ - "XW = np.hstack([X, W])\n", - "XW_train, XW_val, T_train, T_val, Y_train, Y_val = train_test_split(XW, T, y, test_size=.4)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9_X0_AQS9cGc" - }, - "source": [ - "#### Fit all CATE models on train data" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "fNWePOEi9cGc", - "outputId": "1e887546-43fe-4b1f-f238-06ec0d15ab2b" - }, - "source": [ - "from joblib import Parallel, delayed\n", - "\n", - "def fit_model(name, model):\n", - " return name, model.fit(Y_train, T_train, X=XW_train)\n", - "\n", - "models = Parallel(n_jobs=-1, verbose=1)(delayed(fit_model)(name, mdl) for name, mdl in models)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 9 out of 9 | elapsed: 17.7s finished\n" - ], - "name": "stderr" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5HrON5yV9cGc" - }, - "source": [ - "#### Train the scorer on the validation data" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "B4r0CXBP9cGc", - "outputId": "aae0216f-9ed1-4846-d0d4-eaff59d12e77" - }, - "source": [ - "from econml.score import RScorer\n", - "\n", - "# Causal score actually needs fitting on the test set!\n", - "scorer = RScorer(model_y=reg(), model_t=clf(),\n", - " discrete_treatment=True, cv=3,\n", - " mc_iters=3, mc_agg='median')\n", - "scorer.fit(Y_val, T_val, X=XW_val)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 51 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VaApR6459cGd" - }, - "source": [ - "#### Evaluate each of the trained CATE models on the validation data" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "WX92N2EP9cGd" - }, - "source": [ - "# Then we can evaluate every trained CATE model\n", - "rscore = [scorer.score(mdl) for _, mdl in models]" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "E2ZWa1Mu9cGd" - }, - "source": [ - "#### Calculate ideal score of each model, since we know ground truth" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "1e2SvCV89cGd" - }, - "source": [ - "expected_te_val = 1 + XW_val[:, 0]" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "yPF1_UOM9cGe" - }, - "source": [ - "rootpehe = [np.sqrt(np.mean((expected_te_val.flatten() - mdl.effect(XW_val).flatten())**2))\n", - " for _, mdl in models]" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "a4RafR039cGe" - }, - "source": [ - "#### Qualitatively different performance of each method" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 948 - }, - "id": "fkIgNP999cGe", - "outputId": "3aaa0a23-a756-4857-9daa-0f94c68b7280" - }, - "source": [ - "plt.figure(figsize=(16, 16))\n", - "rows = int(np.ceil(len(models) / 3))\n", - "for it, (name, mdl) in enumerate(models):\n", - " plt.subplot(rows, 3, it + 1)\n", - " plt.title('{}. RScore: {:.3f}, Root-PEHE: {:.3f}'.format(name, rscore[it], rootpehe[it]))\n", - " plt.scatter(XW_val[:, 0], mdl.effect(XW_val), label='{}'.format(name))\n", - " plt.plot(XW_val[:, 0], 1 + XW_val[:, 0], 'b--', label='True effect')\n", - " plt.ylabel('Treatment Effect')\n", - " plt.xlabel('x')\n", - " plt.legend()\n", - "plt.show()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hFddbtTU9cGf" - }, - "source": [ - "#### RScore correlates well with ideal score\n", - "\n", - "Higher `Rscore` implies smaller `PEHE`" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 279 - }, - "id": "i2w9dPuI9cGf", - "outputId": "3adc25ea-2f29-46e2-be2e-4a5162994be3" - }, - "source": [ - "plt.scatter(rootpehe, rscore)\n", - "plt.xlabel('rpehe')\n", - "plt.ylabel('rscore')\n", - "plt.show()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "r8eRryFl9cGf" - }, - "source": [ - "#### Choose CATE model with larger Rscore" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "wTaPVNH39cGf", - "outputId": "bb85de97-45cd-4a07-d659-38bc56183bfb" - }, - "source": [ - "mdl, score = scorer.best_model([mdl for _, mdl in models])\n", - "mdl" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 57 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "cULnL_mZ9cGg" - }, - "source": [ - "rootpehe_best = np.sqrt(np.mean((expected_te_val.flatten() - mdl.effect(XW_val).flatten())**2))" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 295 - }, - "id": "kLwPBbku9cGg", - "outputId": "125c9a16-1d42-4800-8ee5-c77c24d330f1" - }, - "source": [ - "plt.figure()\n", - "plt.title('RScore: {:.3f}, Root-PEHE: {:.3f}'.format(score, rootpehe_best))\n", - "plt.scatter(XW_val[:, 0], mdl.effect(XW_val), label='best')\n", - "plt.plot(XW_val[:, 0], 1 + XW_val[:, 0], 'b--', label='True effect')\n", - "plt.ylabel('Treatment Effect')\n", - "plt.xlabel('x')\n", - "plt.legend()\n", - "plt.show()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AWpMuY_49cGg" - }, - "source": [ - "# 4. Interpretation" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "PkjmyQgj9cGg" - }, - "source": [ - "y, T, X, W = gen_data(2000, discrete=True)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "S53DNzMd9cGg" - }, - "source": [ - "#### Fit any CATE model" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "XoHzf8Lo9cGg", - "outputId": "e5b36880-2294-4fbb-d4f7-b4e2535f1d9b" - }, - "source": [ - "from econml.dml import NonParamDML\n", - "\n", - "est = NonParamDML(model_y=RandomForestRegressor(min_samples_leaf=10), # Any ML model for E[Y|X,W]\n", - " model_t=RandomForestClassifier(min_samples_leaf=10), # Any ML model for E[T|X,W]\n", - " model_final=RandomForestRegressor(max_depth=2), # Any ML model for CATE\n", - " discrete_treatment=True, # categorical or continuous treatment\n", - " cv=5, # number of crossfit folds\n", - " mc_iters=1) # repetitions of cross-fitting for stability\n", - "\n", - "est.fit(y, T, X=X, W=W, cache_values=True) # fit the CATE model" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 61 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2N5UElbB9cGh" - }, - "source": [ - "#### Interpret its behavior with a single Tree" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "YnMz_1Hr9cGh", - "outputId": "cbca0155-0c49-424b-9366-c0496df4e3b5" - }, - "source": [ - "from econml.cate_interpreter import SingleTreeCateInterpreter\n", - "\n", - "intrp = SingleTreeCateInterpreter(max_depth=1)\n", - "intrp.interpret(est, X)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 62 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "JVQosW2K9cGh" - }, - "source": [ - "intrp.export_graphviz(out_file='cate_tree.dot')" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 248 - }, - "id": "Rvq9jg649cGh", - "outputId": "b43e7d52-09d1-45cd-b21f-08d2f51cfa55" - }, - "source": [ - "intrp.plot()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WCtJ48zI9cGi" - }, - "source": [ - "#### Make tree-based policy recommendations from CATE model" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SZL7vB1_9cGi", - "outputId": "b085ab61-7631-4959-b5e8-ed458e8e80d1" - }, - "source": [ - "from econml.cate_interpreter import SingleTreePolicyInterpreter\n", - "\n", - "intrp = SingleTreePolicyInterpreter(max_depth=1)\n", - "intrp.interpret(est, X, sample_treatment_costs=0.2)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 65 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "-tWTr9ha9cGi" - }, - "source": [ - "intrp.export_graphviz(out_file='policy_tree.dot')" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 279 - }, - "id": "J90REc4o9cGi", - "outputId": "44988977-d39f-4fec-cca2-fe02014c6bc9" - }, - "source": [ - "intrp.plot()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YMzp0si39cGj" - }, - "source": [ - "#### Interpret CATE model with SHAP" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "FS1Uy2V-9cGj" - }, - "source": [ - "shap_values = est.shap_values(X)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 194 - }, - "id": "cXdMGuBY9cGj", - "outputId": "f980abf2-c1ed-4305-f76c-48fae99331d8" - }, - "source": [ - "import shap\n", - "\n", - "# effect heterogeneity feature importances with summary plot\n", - "shap.summary_plot(shap_values['Y0']['T0_1'])" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 193 - }, - "id": "rowyWQ_l9cGj", - "outputId": "0dc74436-6ca5-417b-e45c-b260c20b6e19" - }, - "source": [ - "shap.initjs()\n", - "# explain the heterogeneity of the effect of any single sample\n", - "shap.force_plot(shap_values['Y0']['T0_1'][0])" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "
\n", - "
\n", - " Visualization omitted, Javascript library not loaded!
\n", - " Have you run `initjs()` in this notebook? If this notebook was from another\n", - " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", - " this notebook on github the Javascript has been stripped for security. If you are using\n", - " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", - "
\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 70 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dA1nRbFK9cGk" - }, - "source": [ - "# 5. Validation and Sensitivity" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Nds7Fwyg9cGl" - }, - "source": [ - "y, T, Z, X, W = gen_data_iv(2000)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-zHcWkF19cGl" - }, - "source": [ - "#### Instantiate any CATE model" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "0nMA97Jh9cGl" - }, - "source": [ - "from econml.iv.dml import OrthoIV\n", - "\n", - "est = OrthoIV(model_y_xw=RandomForestRegressor(), # ML model for E[Y|X,W]\n", - " model_t_xw=RandomForestRegressor(), # ML model for E[Y|X,W]\n", - " model_z_xw=RandomForestRegressor(), # ML model for E[Y|X,W]\n", - " discrete_treatment=False, # categorical/continuous treatment\n", - " discrete_instrument=False, # categorical/continuous instrument\n", - " cv=2, # number of crossfit folds\n", - " mc_iters=1) # repetitions of cross-fitting for stability" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Z91lWEiQ9cGl" - }, - "source": [ - "#### Enable dowhy capabilities" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "QEe9XMai9cGm" - }, - "source": [ - "import dowhy\n", - "est = est.dowhy" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BIWgh7Ce9cGm" - }, - "source": [ - "#### Then fit" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "roPf-t439cGm", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "4d24cac9-58fa-44fa-87e9-f8e57ec68176" - }, - "source": [ - "est.fit(y, T, Z=Z, X=X, W=W, cache_values=True)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 74 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5tlyZMcP9cGn" - }, - "source": [ - "#### Use it as a normal EconML cate estimator" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "lHr9Ooaa9cGn", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 290 - }, - "outputId": "e86a4da8-cbac-4271-ae99-8d312fd1949c" - }, - "source": [ - "est.summary()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 0.99 0.035 27.933 0.0 0.932 1.049
X1 -0.012 0.027 -0.441 0.659 -0.057 0.033
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CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 1.018 0.026 38.577 0.0 0.975 1.061


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------\n", - "X0 0.99 0.035 27.933 0.0 0.932 1.049\n", - "X1 -0.012 0.027 -0.441 0.659 -0.057 0.033\n", - " CATE Intercept Results \n", - "====================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------------------\n", - "cate_intercept 1.018 0.026 38.577 0.0 0.975 1.061\n", - "--------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 75 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "o1z0z-E79cGo", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 235 - }, - "outputId": "5cf7bc63-16c6-4eb4-9cd7-3ecd7a7128fd" - }, - "source": [ - "est.effect_inference(X[:5]).summary_frame()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "0 0.286 0.045 6.362 0.0 0.212 0.360\n", - "1 2.730 0.071 38.618 0.0 2.613 2.846\n", - "2 0.678 0.030 22.866 0.0 0.630 0.727\n", - "3 0.760 0.038 19.951 0.0 0.697 0.823\n", - "4 2.539 0.086 29.605 0.0 2.398 2.680" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 76 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cAPR5eMZ9cGo" - }, - "source": [ - "#### But now we also have DoWhy capabilities: Sensitivity Analysis" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "9aUCWN489cGo" - }, - "source": [ - "ref_res = est.refute_estimate(method_name=\"add_unobserved_common_cause\",\n", - " effect_strenght_on_treatment=0.05,\n", - " effect_strength_on_outcome=0.5)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "7bTLzM_H9cGp", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "7b5fdb67-e26d-46f0-b46d-572001977533" - }, - "source": [ - "print(ref_res)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Refute: Add an Unobserved Common Cause\n", - "Estimated effect:1.0314669223854354\n", - "New effect:0.9983858330683743\n", - "\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MuaLIl6X9cGp" - }, - "source": [ - "# 6. Policy Learning" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Z5NSUCzK9cGp" - }, - "source": [ - "y, T, X, W = gen_data(2000, discrete=True)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_oGYT0HP9cGp" - }, - "source": [ - "#### Fit a Doubly Robust policy tree" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "PLk_-21M9cGp" - }, - "source": [ - "from econml.policy import DRPolicyTree\n", - "\n", - "est = DRPolicyTree(max_depth=2, min_impurity_decrease=0.01, honest=True)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "DVBwu-oh9cGq", - "outputId": "8972c75a-1793-4611-959c-708e85ea5796" - }, - "source": [ - "est.fit(y, T, X=X, W=W)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 81 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WNpPMq0Z9cGq" - }, - "source": [ - "#### Visualize treatment policy" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 279 - }, - "id": "4YnPJkJW9cGq", - "outputId": "5b9b02ea-01dd-4c3a-d598-4fb61b8e02ce" - }, - "source": [ - "est.plot()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JjBym5Iw9cGr", - "outputId": "2fdf00f7-9f6f-494e-be9a-84b85ad7d30c" - }, - "source": [ - "est.feature_importances_" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([1., 0.])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 83 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "L7q7UxZG9cGr" - }, - "source": [ - "#### Produce recommended treatments" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "sZ_E-Tjk9cGr", - "outputId": "4773b57f-bc7e-4f70-f65c-50ad9471041e" - }, - "source": [ - "est.predict(X[:100])" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n", - " 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n", - " 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 84 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Zb3MFbCq9cGs" - }, - "source": [ - "#### Fit a Doubly Robust policy forest" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "N4CAMtPQ9cGs" - }, - "source": [ - "from econml.policy import DRPolicyForest\n", - "\n", - "est = DRPolicyForest(n_estimators=100, max_depth=2,\n", - " min_impurity_decrease=0.01, honest=True)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "jK5vBCCm9cGs", - "outputId": "1b97cc54-6324-42c0-e403-fba3dc3dee14" - }, - "source": [ - "est.fit(y, T, X=X, W=W)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 86 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lhiGgCKD9cGt" - }, - "source": [ - "#### Produce recommended treatments" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "lAGuJ1wB9cGt", - "outputId": "b1590aad-d1d1-4657-8e65-012ae7abec58" - }, - "source": [ - "est.predict(X[:100])" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n", - " 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 87 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3xWXtC8k9cGt" - }, - "source": [ - "#### Plot one of the trees" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 279 - }, - "id": "Xen6bm7Y9cGt", - "outputId": "25f978fc-a5d0-4395-9bb2-a0b089ff2828" - }, - "source": [ - "est.plot(0)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HInWzLf-84ta" - }, - "source": [ - "#### Plot decisions as function of covariates" - ] - }, - { - "cell_type": "code", - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, "colab": { - "base_uri": "https://localhost:8080/", - "height": 265 - }, - "id": "0EdmyR-39cGt", - "outputId": "992dd9e8-abed-4f2b-a3cd-2d12e507ed05" - }, - "source": [ - "plt.scatter(X[:, 0], est.predict(X))\n", - "plt.show()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jYAHRpIF-BRW" + }, + "source": [ + "# **KDD2021 Tutorial:** [Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber](https://causal-machine-learning.github.io/kdd2023-workshop/)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AYlHqLQf-PjP" + }, + "source": [ + "# Introduction to [EconML](https://github.com/microsoft/EconML)\n", + "\n", + "A python library for estimation of heterogeneous treatment effects with Machine Learning.\n", + "\n", + "**Presentation:** [Introduction to EconML](https://drive.google.com/file/d/1gt4KNznrYbwdryi9jGcC0-hDCNg7mBNE/view?usp=sharing)\n", + "\n", + "**Github:** https://github.com/microsoft/EconML\n", + "\n", + "**Documentation:** https://econml.azurewebsites.net/\n", + "\n", + "By the Microsoft Research project [ALICE (Automated Learning and Intelligence for Causation and Economics)](https://www.microsoft.com/en-us/research/project/alice/)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cEqRBSJV9dTX" + }, + "source": [ + "#!pip install econml" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "yPw9zWWm3H9e" + }, + "source": [ + "!pip install git+https://github.com/microsoft/EconML.git@mehei/driv#egg=econml" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "PKxsq0Mv9cGD" + }, + "source": [ + "import numpy as np\n", + "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", + "from sklearn.linear_model import LassoCV, Lasso\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.model_selection import train_test_split\n", + "import matplotlib.pyplot as plt\n", + "import scipy\n", + "import warnings\n", + "warnings.simplefilter('ignore')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "WNvGZiKF9cGG" + }, + "source": [ + "def gen_data(n, discrete=False):\n", + " X = np.random.normal(0, 1, size=(n, 2))\n", + " W = np.random.normal(0, 1, size=(n, 2))\n", + " if discrete:\n", + " T = np.random.binomial(1, scipy.special.expit(W[:, 0]))\n", + " else:\n", + " T = W[:, 0] + np.random.normal(0, 1, size=(n,))\n", + " y = (X[:, 0] + 1) * T + W[:, 0] + np.random.normal(0, 1, size=(n,))\n", + " return y, T, X, W\n", + "\n", + "def gen_data_iv(n):\n", + " X = np.random.normal(0, 1, size=(n, 2))\n", + " W = np.random.normal(0, 1, size=(n, 2))\n", + " U = np.random.normal(0, 1, size=(n,))\n", + " Z = np.random.normal(0, 1, size=(n,))\n", + " T = Z + W[:, 0] + U\n", + " y = (X[:, 0] + 1) * T + W[:, 0] + U\n", + " return y, T, Z, X, W" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7xzAqDMb9cGH" + }, + "source": [ + "# 1. Estimation under Exogeneity" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "TuRauvKA9cGH" + }, + "source": [ + "y, T, X, W = gen_data(1000)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nHhjI8Kb9cGI", + "outputId": "059fd233-bd8e-4de3-fa61-1a042b381aba" + }, + "source": [ + "from econml.dml import NonParamDML\n", + "\n", + "est = NonParamDML(model_y=RandomForestRegressor(), # Any ML model for E[Y|X,W]\n", + " model_t=RandomForestRegressor(), # Any ML model for E[T|X,W]\n", + " model_final=RandomForestRegressor(max_depth=2), # Any ML model for CATE\n", + " discrete_treatment=False, # categorical or continuous treatment\n", + " cv=2, # number of crossfit folds\n", + " mc_iters=1) # repetitions of cross-fitting for stability\n", + "\n", + "est.fit(y, T, X=X, W=W, cache_values=True) # fit the CATE model" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Gc4fpcMr9cGJ" + }, + "source": [ + "#### Personalized effect estimates on test samples" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9rqqLxnt9cGJ", + "outputId": "cd71c483-e9e1-48f8-fab7-e8534777a008" + }, + "source": [ + "# personalized effect for each sample from going from treatment 0 to treatment level 1\n", + "est.effect(X[:5], T0=0, T1=1)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 1.54592291, 0.87946172, -0.47128723, 0.81285758, 3.1442986 ])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qC23J2R09cGJ" + }, + "source": [ + "#### ML model diagnostics" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yE2v-YzX9cGK", + "outputId": "6d074083-9a40-4222-d7bb-f2d158fc2df0" + }, + "source": [ + "# fitted nuisance models for each cross-fitting fold and out-of-sample scores\n", + "est.models_y, est.nuisance_scores_y" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "([[RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " max_samples=None, min_impurity_decrease=0.0,\n", + " min_impurity_split=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, n_jobs=None, oob_score=False,\n", + " random_state=None, verbose=0, warm_start=False),\n", + " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " max_samples=None, min_impurity_decrease=0.0,\n", + " min_impurity_split=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, n_jobs=None, oob_score=False,\n", + " random_state=None, verbose=0, warm_start=False)]],\n", + " [[0.5409335662361056, 0.48599679070328405]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JXOd9Rfa9cGK", + "outputId": "dfbebf0a-19e9-4f15-9182-e22318015360" + }, + "source": [ + "est.models_t, est.nuisance_scores_t" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "([[RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " max_samples=None, min_impurity_decrease=0.0,\n", + " min_impurity_split=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, n_jobs=None, oob_score=False,\n", + " random_state=None, verbose=0, warm_start=False),\n", + " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " max_samples=None, min_impurity_decrease=0.0,\n", + " min_impurity_split=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, n_jobs=None, oob_score=False,\n", + " random_state=None, verbose=0, warm_start=False)]],\n", + " [[0.4282760371961908, 0.3782367983417111]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CLbqvUvr9cGK" + }, + "source": [ + "#### CATE model diagnostics" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IdiMMpsQ9cGL", + "outputId": "6b6277ca-32b3-4d29-f5f4-37b765551e39" + }, + "source": [ + "# in-sample goodness-of-fit score for the final cate model\n", + "print(est.score_)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "1.4717907756757855\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "968ktBK_9cGL" + }, + "source": [ + "#### Nuisance quantity diagnostics" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "KDwBlm-i9cGL" + }, + "source": [ + "# calculated residuals for each training sample\n", + "yres, Tres, X_cache, W_cache = est.residuals_" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Jkc1WSi19cGL" + }, + "source": [ + "# 2. Estimation with Instruments" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8pjyjlRQ9cGM" + }, + "source": [ + "y, T, Z, X, W = gen_data_iv(2000)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "l5gr5sGS9cGM" + }, + "source": [ + "from econml.iv.dml import OrthoIV\n", + "\n", + "est = OrthoIV(model_y_xw=RandomForestRegressor(), # ML model for E[Y|X,W]\n", + " model_t_xw=RandomForestRegressor(), # ML model for E[T|X,W]\n", + " model_z_xw=RandomForestRegressor(), # ML model for E[Z|X,W]\n", + " discrete_treatment=False, # categorical/continuous treatment\n", + " discrete_instrument=False, # categorical/continuous instrument\n", + " cv=2, # number of crossfit folds\n", + " mc_iters=1) # repetitions of cross-fitting for stability" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "b_k1NpEp9cGM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8b057fc5-bd87-43fa-ea75-33ee736a2a88" + }, + "source": [ + "est.fit(y, T, Z=Z, X=X, W=W, cache_values=True)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a0zr3m3r9cGM" + }, + "source": [ + "#### Personalized effect estimates on test samples" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2wAnhhq99cGN", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "2061468c-6969-4b1a-86fa-ba16a8f6b49c" + }, + "source": [ + "est.effect(X, T0=0, T1=1)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([-0.32390668, 2.23728003, 0.70346148, ..., 0.85984079,\n", + " 0.17539819, 1.07325619])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D9ESfbo29cGN" + }, + "source": [ + "#### ML model diagnostics" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9JlbW4an9cGN", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f77e36a8-295f-4dff-e11d-21057357e4c8" + }, + "source": [ + "est.models_y_xw, est.nuisance_scores_y_xw" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "([[RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " max_samples=None, min_impurity_decrease=0.0,\n", + " min_impurity_split=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, n_jobs=None, oob_score=False,\n", + " random_state=None, verbose=0, warm_start=False),\n", + " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " max_samples=None, min_impurity_decrease=0.0,\n", + " min_impurity_split=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, n_jobs=None, oob_score=False,\n", + " random_state=None, verbose=0, warm_start=False)]],\n", + " [[0.3493220983540998, 0.2621905464991312]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "e1YCQWZK9cGO", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "71292840-3f77-4985-fdf3-855ad7d4f07a" + }, + "source": [ + "est.models_t_xw, est.nuisance_scores_t_xw" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "([[RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " max_samples=None, min_impurity_decrease=0.0,\n", + " min_impurity_split=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, n_jobs=None, oob_score=False,\n", + " random_state=None, verbose=0, warm_start=False),\n", + " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " max_samples=None, min_impurity_decrease=0.0,\n", + " min_impurity_split=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, n_jobs=None, oob_score=False,\n", + " random_state=None, verbose=0, warm_start=False)]],\n", + " [[0.24243204274642238, 0.22952337252664082]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cN5Ga_YH9cGO", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fca11455-0e2c-4881-96a9-0e07a67d4461" + }, + "source": [ + "est.models_z_xw, est.nuisance_scores_z_xw" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "([[RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " max_samples=None, min_impurity_decrease=0.0,\n", + " min_impurity_split=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, n_jobs=None, oob_score=False,\n", + " random_state=None, verbose=0, warm_start=False),\n", + " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " max_samples=None, min_impurity_decrease=0.0,\n", + " min_impurity_split=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, n_jobs=None, oob_score=False,\n", + " random_state=None, verbose=0, warm_start=False)]],\n", + " [[-0.10057707531258632, -0.08476759034439452]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oNkC5W9i9cGP" + }, + "source": [ + "#### CATE model diagnostics" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zENiyXuV9cGP", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "621aba3d-e433-4c89-e0dc-1879afd565f6" + }, + "source": [ + "print(est.score_)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "5.018208071305707e-16\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "slJuNhWU9cGP" + }, + "source": [ + "#### Nuisance quantity diagnostics" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1o5aP05y9cGP" + }, + "source": [ + "yres, Tres, Zres, Xc, Wc, Zc = est.residuals_" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WrsMl9pm9cGQ" + }, + "source": [ + "# 3. Inference" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "oeTIUL6_9cGQ" + }, + "source": [ + "y, T, X, W = gen_data(1000)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K0kinaK__Djv" + }, + "source": [ + "### Generic Bootstrap Inference" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "rSGauZ1k9cGQ" + }, + "source": [ + "from econml.dml import NonParamDML\n", + "from econml.sklearn_extensions.linear_model import WeightedLasso\n", + "\n", + "est = NonParamDML(model_y=Lasso(alpha=.1), # Any ML model for E[Y|X,W]\n", + " model_t=Lasso(alpha=.1), # Any ML model for E[T|X,W]\n", + " model_final=WeightedLasso(alpha=.1), # Any ML model for CATE that accepts `sample_weight` at fit\n", + " discrete_treatment=False, # categorical or continuous treatment\n", + " cv=2, # number of crossfit folds\n", + " mc_iters=1) # repetitions of cross-fitting for stability" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lckeXBSV9cGR", + "outputId": "d1cecd80-0022-4e16-c4a9-dc1081f474f3" + }, + "source": [ + "est.fit(y, T, X=X, W=W, inference='bootstrap') # fit the CATE model" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 23 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + }, + "id": "p-RP37i-9cGR", + "outputId": "2126735a-91d6-4908-eb8a-b36bcae1cbc8" + }, + "source": [ + "est.effect_inference(X[:5], T0=0, T1=1).summary_frame()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
01.2700.03932.6200.01.2101.334
11.7070.05133.6340.01.6311.787
22.4170.07930.4280.02.3042.559
31.3740.04133.7000.01.3121.445
4-0.3020.070-4.3380.0-0.449-0.216
\n", + "
" + ], + "text/plain": [ + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "X \n", + "0 1.270 0.039 32.620 0.0 1.210 1.334\n", + "1 1.707 0.051 33.634 0.0 1.631 1.787\n", + "2 2.417 0.079 30.428 0.0 2.304 2.559\n", + "3 1.374 0.041 33.700 0.0 1.312 1.445\n", + "4 -0.302 0.070 -4.338 0.0 -0.449 -0.216" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 24 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 251 + }, + "id": "9tGAONKF9cGS", + "outputId": "cce9a4d0-d2a9-4991-87aa-90504695a8a9" + }, + "source": [ + "est.ate_inference(X)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Uncertainty of Mean Point Estimate
mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper
0.989 0.061 16.1 0.0 0.888 1.09
\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Distribution of Point Estimate
std_point pct_point_lower pct_point_upper
1.041 -0.704 2.734
\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Total Variance of Point Estimate
stderr_point ci_point_lower ci_point_upper
1.042 -0.698 2.737


Note: The stderr_mean is a conservative upper bound." + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 25 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ab22GTTm9cGS", + "outputId": "b26f4c08-1938-43fb-c81b-36ca177254f8" + }, + "source": [ + "from econml.inference import BootstrapInference\n", + "est.fit(y, T, X=X, W=W,\n", + " inference=BootstrapInference(n_bootstrap_samples=100,\n", + " bootstrap_type='normal'))" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 26 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + }, + "id": "MK02FTmf9cGS", + "outputId": "3d1bb9f4-cbcf-45ac-930b-f6d837b407b0" + }, + "source": [ + "est.effect_inference(X[:5], T0=0, T1=1).summary_frame()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
01.2670.04627.5910.01.1921.343
11.7040.05530.9780.01.6131.794
22.4140.07930.6230.02.2842.543
31.3720.04828.8790.01.2941.450
4-0.3030.073-4.1250.0-0.424-0.182
\n", + "
" + ], + "text/plain": [ + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "X \n", + "0 1.267 0.046 27.591 0.0 1.192 1.343\n", + "1 1.704 0.055 30.978 0.0 1.613 1.794\n", + "2 2.414 0.079 30.623 0.0 2.284 2.543\n", + "3 1.372 0.048 28.879 0.0 1.294 1.450\n", + "4 -0.303 0.073 -4.125 0.0 -0.424 -0.182" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 27 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 251 + }, + "id": "qIKkO36S9cGT", + "outputId": "574a2f94-2cca-463f-adf6-b2193d1d0e6c" + }, + "source": [ + "est.ate_inference(X)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Uncertainty of Mean Point Estimate
mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper
0.986 0.065 15.226 0.0 0.88 1.093
\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Distribution of Point Estimate
std_point pct_point_lower pct_point_upper
1.039 -0.704 2.73
\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Total Variance of Point Estimate
stderr_point ci_point_lower ci_point_upper
1.041 -0.703 2.735


Note: The stderr_mean is a conservative upper bound." + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 28 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LH0Uo_yJ--G_" + }, + "source": [ + "### Tailored Valid Inference" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZfUSZoW-4TJQ" + }, + "source": [ + "#### Heteroskedasticity-robust OLS inference for linear CATE models $\\theta(x)=\\langle\\theta, \\phi(x)\\rangle$" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uPiPxMU19cGT" + }, + "source": [ + "from econml.dml import LinearDML\n", + "\n", + "est = LinearDML(model_y=RandomForestRegressor(), # Any ML model for E[Y|X,W]\n", + " model_t=RandomForestRegressor(), # Any ML model for E[T|X,W]\n", + " featurizer=PolynomialFeatures(degree=2, include_bias=False)) # any featurizer for " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qAQKnPA59cGU", + "outputId": "77583b37-6895-4c2e-c636-d948a6de8798" + }, + "source": [ + "est.fit(y, T, X=X, W=W)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 30 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 353 + }, + "id": "Cy73CEcm9cGU", + "outputId": "749cefb2-54d6-475a-d630-4a8d9beba4f6" + }, + "source": [ + "est.summary()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 1.001 0.034 29.398 0.0 0.945 1.057
X1 0.042 0.032 1.302 0.193 -0.011 0.094
X0^2 0.011 0.025 0.447 0.655 -0.029 0.052
X0 X1 0.032 0.027 1.203 0.229 -0.012 0.076
X1^2 -0.024 0.019 -1.269 0.204 -0.056 0.007
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CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 0.99 0.046 21.503 0.0 0.914 1.066


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Coefficient Results \n", + "===========================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "-----------------------------------------------------------\n", + "X0 1.001 0.034 29.398 0.0 0.945 1.057\n", + "X1 0.042 0.032 1.302 0.193 -0.011 0.094\n", + "X0^2 0.011 0.025 0.447 0.655 -0.029 0.052\n", + "X0 X1 0.032 0.027 1.203 0.229 -0.012 0.076\n", + "X1^2 -0.024 0.019 -1.269 0.204 -0.056 0.007\n", + " CATE Intercept Results \n", + "====================================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "--------------------------------------------------------------------\n", + "cate_intercept 0.99 0.046 21.503 0.0 0.914 1.066\n", + "--------------------------------------------------------------------\n", + "\n", + "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", + "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", + "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", + "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", + "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", + "\"\"\"" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 38 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + }, + "id": "og2hubAj9cGV", + "outputId": "5f0c835f-dcff-4f9c-d1be-dc6741da2a10" + }, + "source": [ + "est.effect_inference(X[:5], T0=0, T1=1).summary_frame()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
01.2610.04726.7320.01.1841.339
11.7850.06826.2740.01.6731.897
22.4160.08329.0690.02.2802.553
31.4180.04829.8280.01.3401.497
4-0.2720.064-4.2660.0-0.378-0.167
\n", + "
" + ], + "text/plain": [ + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "X \n", + "0 1.261 0.047 26.732 0.0 1.184 1.339\n", + "1 1.785 0.068 26.274 0.0 1.673 1.897\n", + "2 2.416 0.083 29.069 0.0 2.280 2.553\n", + "3 1.418 0.048 29.828 0.0 1.340 1.497\n", + "4 -0.272 0.064 -4.266 0.0 -0.378 -0.167" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 32 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aMqhTNEb4Zt4" + }, + "source": [ + "#### Debiased Lasso Inference for high-dimensional linear CATE models $\\theta(x)=\\langle\\theta, \\phi(x)\\rangle$" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "dtjN8aC99cGY" + }, + "source": [ + "from econml.dml import SparseLinearDML\n", + "\n", + "est = SparseLinearDML(featurizer=PolynomialFeatures(degree=3, include_bias=False))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "W3xElnFJ9cGY", + "outputId": "89e6d659-d695-49c0-927c-66e3128c3e13" + }, + "source": [ + "est.fit(y, T, X=X, W=W)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 40 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 437 + }, + "id": "6XHdjW9I9cGY", + "outputId": "b8ad3ad6-7006-47ac-c431-d90c109cf0e3" + }, + "source": [ + "est.summary()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 0.99 0.065 15.182 0.0 0.882 1.097
X1 0.012 0.07 0.179 0.858 -0.102 0.127
X0^2 0.071 0.022 3.168 0.002 0.034 0.107
X0 X1 -0.012 0.034 -0.337 0.736 -0.068 0.045
X1^2 -0.019 0.027 -0.686 0.493 -0.063 0.026
X0^3 0.004 0.013 0.328 0.743 -0.017 0.026
X0^2 X1 0.026 0.025 1.025 0.305 -0.016 0.067
X0 X1^2 0.021 0.026 0.806 0.42 -0.022 0.065
X1^3 0.001 0.016 0.054 0.957 -0.026 0.027
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CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 0.945 0.051 18.628 0.0 0.862 1.029


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Coefficient Results \n", + "=============================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "-------------------------------------------------------------\n", + "X0 0.99 0.065 15.182 0.0 0.882 1.097\n", + "X1 0.012 0.07 0.179 0.858 -0.102 0.127\n", + "X0^2 0.071 0.022 3.168 0.002 0.034 0.107\n", + "X0 X1 -0.012 0.034 -0.337 0.736 -0.068 0.045\n", + "X1^2 -0.019 0.027 -0.686 0.493 -0.063 0.026\n", + "X0^3 0.004 0.013 0.328 0.743 -0.017 0.026\n", + "X0^2 X1 0.026 0.025 1.025 0.305 -0.016 0.067\n", + "X0 X1^2 0.021 0.026 0.806 0.42 -0.022 0.065\n", + "X1^3 0.001 0.016 0.054 0.957 -0.026 0.027\n", + " CATE Intercept Results \n", + "====================================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "--------------------------------------------------------------------\n", + "cate_intercept 0.945 0.051 18.628 0.0 0.862 1.029\n", + "--------------------------------------------------------------------\n", + "\n", + "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", + "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", + "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", + "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", + "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", + "\"\"\"" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 41 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + }, + "id": "nYXaiMBX9cGZ", + "outputId": "34e12072-ae19-40c5-d75f-72d96ff4682c" + }, + "source": [ + "est.effect_inference(X[:5], T0=0, T1=1).summary_frame()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
01.2440.06120.4580.0001.1441.344
11.7430.10217.1200.0001.5751.910
22.5280.09227.5530.0002.3772.679
31.3660.06122.5210.0001.2661.466
4-0.2360.085-2.7780.005-0.376-0.096
\n", + "
" + ], + "text/plain": [ + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "X \n", + "0 1.244 0.061 20.458 0.000 1.144 1.344\n", + "1 1.743 0.102 17.120 0.000 1.575 1.910\n", + "2 2.528 0.092 27.553 0.000 2.377 2.679\n", + "3 1.366 0.061 22.521 0.000 1.266 1.466\n", + "4 -0.236 0.085 -2.778 0.005 -0.376 -0.096" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 42 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9kI_3o004cnQ" + }, + "source": [ + "#### Bootstrap-of-Little-Bags inference for forests CATE models $\\theta(x)$" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JVKLLU5H9cGZ" + }, + "source": [ + "y, T, X, W = gen_data(2000, discrete=True)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TzNqV3HW9cGZ", + "outputId": "e829ef4f-3016-431a-91d3-4864c9cca580" + }, + "source": [ + "from econml.dml import CausalForestDML\n", + "\n", + "est = CausalForestDML(discrete_treatment=True,\n", + " criterion='mse', n_estimators=1000)\n", + "est.tune(y, T, X=X, W=W)\n", + "est.fit(y, T, X=X, W=W, cache_values=True)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 44 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 451 + }, + "id": "PhogESjH9cGa", + "outputId": "97fea945-cc4e-4641-cb7b-e20f8e61064d" + }, + "source": [ + "est.summary()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Population summary of CATE predictions on Training Data\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Uncertainty of Mean Point Estimate
mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper
1.099 0.205 5.362 0.0 0.762 1.436
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Distribution of Point Estimate
std_point pct_point_lower pct_point_upper
1.086 -0.862 2.947
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Total Variance of Point Estimate
stderr_point ci_point_lower ci_point_upper
1.105 -0.862 2.965
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Doubly Robust ATE on Training Data Results
point_estimate stderr zstat pvalue ci_lower ci_upper
ATE 1.095 0.056 19.388 0.0 1.002 1.187
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Doubly Robust ATT(T=0) on Training Data Results
point_estimate stderr zstat pvalue ci_lower ci_upper
ATT 1.075 0.078 13.719 0.0 0.947 1.204
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Doubly Robust ATT(T=1) on Training Data Results
point_estimate stderr zstat pvalue ci_lower ci_upper
ATT 1.116 0.081 13.721 0.0 0.982 1.25


Note: The stderr_mean is a conservative upper bound." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Uncertainty of Mean Point Estimate \n", + "===============================================================\n", + "mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper\n", + "---------------------------------------------------------------\n", + " 1.099 0.205 5.362 0.0 0.762 1.436\n", + " Distribution of Point Estimate \n", + "=========================================\n", + "std_point pct_point_lower pct_point_upper\n", + "-----------------------------------------\n", + " 1.086 -0.862 2.947\n", + " Total Variance of Point Estimate \n", + "==========================================\n", + "stderr_point ci_point_lower ci_point_upper\n", + "------------------------------------------\n", + " 1.105 -0.862 2.965\n", + " Doubly Robust ATE on Training Data Results \n", + "=========================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "---------------------------------------------------------\n", + "ATE 1.095 0.056 19.388 0.0 1.002 1.187\n", + " Doubly Robust ATT(T=0) on Training Data Results \n", + "=========================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "---------------------------------------------------------\n", + "ATT 1.075 0.078 13.719 0.0 0.947 1.204\n", + " Doubly Robust ATT(T=1) on Training Data Results \n", + "=========================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "---------------------------------------------------------\n", + "ATT 1.116 0.081 13.721 0.0 0.982 1.25\n", + "---------------------------------------------------------\n", + "\n", + "Note: The stderr_mean is a conservative upper bound.\n", + "\"\"\"" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 45 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + }, + "id": "bZzv-0bo9cGa", + "outputId": "26e8e29b-691e-4fa4-d469-7b00ee463ac4" + }, + "source": [ + "est.effect_inference(X[:5], T0=0, T1=1).summary_frame()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
01.9090.2308.2930.0001.5312.288
10.3080.1132.7350.0060.1230.493
2-0.1130.204-0.5530.580-0.4490.223
31.2770.1926.6650.0000.9621.593
42.3950.22510.6390.0002.0242.765
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" + ], + "text/plain": [ + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "X \n", + "0 1.909 0.230 8.293 0.000 1.531 2.288\n", + "1 0.308 0.113 2.735 0.006 0.123 0.493\n", + "2 -0.113 0.204 -0.553 0.580 -0.449 0.223\n", + "3 1.277 0.192 6.665 0.000 0.962 1.593\n", + "4 2.395 0.225 10.639 0.000 2.024 2.765" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 46 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qqO9by3p9cGa" + }, + "source": [ + "# 4. Causal Scoring" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9HwG--DU9cGa" + }, + "source": [ + "y, T, X, W = gen_data(2000, discrete=True)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Rr5El3LR9cGa" + }, + "source": [ + "#### Multitude of approaches for CATE estimation to select from" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "je0AkjG99cGb" + }, + "source": [ + "from econml.dml import DML, LinearDML, SparseLinearDML, NonParamDML\n", + "from econml.metalearners import XLearner, TLearner, SLearner, DomainAdaptationLearner\n", + "from econml.dr import DRLearner\n", + "\n", + "reg = lambda: RandomForestRegressor(min_samples_leaf=10)\n", + "clf = lambda: RandomForestClassifier(min_samples_leaf=10)\n", + "# A multitude of possible approaches for CATE estimation under conditional exogeneity\n", + "models = [('ldml', LinearDML(model_y=reg(), model_t=clf(), discrete_treatment=True,\n", + " linear_first_stages=False, cv=3)),\n", + " ('sldml', SparseLinearDML(model_y=reg(), model_t=clf(), discrete_treatment=True,\n", + " featurizer=PolynomialFeatures(degree=2, include_bias=False),\n", + " linear_first_stages=False, cv=3)),\n", + " ('xlearner', XLearner(models=reg(), cate_models=reg(), propensity_model=clf())),\n", + " ('dalearner', DomainAdaptationLearner(models=reg(), final_models=reg(),\n", + " propensity_model=clf())),\n", + " ('slearner', SLearner(overall_model=reg())),\n", + " ('tlearner', TLearner(models=reg())),\n", + " ('drlearner', DRLearner(model_propensity=clf(), model_regression=reg(),\n", + " model_final=reg(), cv=3)),\n", + " ('rlearner', NonParamDML(model_y=reg(), model_t=clf(), model_final=reg(),\n", + " discrete_treatment=True, cv=3)),\n", + " ('dml3dlasso', DML(model_y=reg(), model_t=clf(), model_final=LassoCV(),\n", + " discrete_treatment=True,\n", + " featurizer=PolynomialFeatures(degree=3),\n", + " linear_first_stages=False, cv=3))\n", + "]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "it8Mb8219cGb" + }, + "source": [ + "#### Split the data in train and validation" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "dsqFr4Cz9cGb" + }, + "source": [ + "XW = np.hstack([X, W])\n", + "XW_train, XW_val, T_train, T_val, Y_train, Y_val = train_test_split(XW, T, y, test_size=.4)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9_X0_AQS9cGc" + }, + "source": [ + "#### Fit all CATE models on train data" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fNWePOEi9cGc", + "outputId": "1e887546-43fe-4b1f-f238-06ec0d15ab2b" + }, + "source": [ + "from joblib import Parallel, delayed\n", + "\n", + "def fit_model(name, model):\n", + " return name, model.fit(Y_train, T_train, X=XW_train)\n", + "\n", + "models = Parallel(n_jobs=-1, verbose=1)(delayed(fit_model)(name, mdl) for name, mdl in models)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 9 out of 9 | elapsed: 17.7s finished\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5HrON5yV9cGc" + }, + "source": [ + "#### Train the scorer on the validation data" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "B4r0CXBP9cGc", + "outputId": "aae0216f-9ed1-4846-d0d4-eaff59d12e77" + }, + "source": [ + "from econml.score import RScorer\n", + "\n", + "# Causal score actually needs fitting on the test set!\n", + "scorer = RScorer(model_y=reg(), model_t=clf(),\n", + " discrete_treatment=True, cv=3,\n", + " mc_iters=3, mc_agg='median')\n", + "scorer.fit(Y_val, T_val, X=XW_val)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 51 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VaApR6459cGd" + }, + "source": [ + "#### Evaluate each of the trained CATE models on the validation data" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WX92N2EP9cGd" + }, + "source": [ + "# Then we can evaluate every trained CATE model\n", + "rscore = [scorer.score(mdl) for _, mdl in models]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E2ZWa1Mu9cGd" + }, + "source": [ + "#### Calculate ideal score of each model, since we know ground truth" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1e2SvCV89cGd" + }, + "source": [ + "expected_te_val = 1 + XW_val[:, 0]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "yPF1_UOM9cGe" + }, + "source": [ + "rootpehe = [np.sqrt(np.mean((expected_te_val.flatten() - mdl.effect(XW_val).flatten())**2))\n", + " for _, mdl in models]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a4RafR039cGe" + }, + "source": [ + "#### Qualitatively different performance of each method" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 948 + }, + "id": "fkIgNP999cGe", + "outputId": "3aaa0a23-a756-4857-9daa-0f94c68b7280" + }, + "source": [ + "plt.figure(figsize=(16, 16))\n", + "rows = int(np.ceil(len(models) / 3))\n", + "for it, (name, mdl) in enumerate(models):\n", + " plt.subplot(rows, 3, it + 1)\n", + " plt.title('{}. RScore: {:.3f}, Root-PEHE: {:.3f}'.format(name, rscore[it], rootpehe[it]))\n", + " plt.scatter(XW_val[:, 0], mdl.effect(XW_val), label='{}'.format(name))\n", + " plt.plot(XW_val[:, 0], 1 + XW_val[:, 0], 'b--', label='True effect')\n", + " plt.ylabel('Treatment Effect')\n", + " plt.xlabel('x')\n", + " plt.legend()\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hFddbtTU9cGf" + }, + "source": [ + "#### RScore correlates well with ideal score\n", + "\n", + "Higher `Rscore` implies smaller `PEHE`" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279 + }, + "id": "i2w9dPuI9cGf", + "outputId": "3adc25ea-2f29-46e2-be2e-4a5162994be3" + }, + "source": [ + "plt.scatter(rootpehe, rscore)\n", + "plt.xlabel('rpehe')\n", + "plt.ylabel('rscore')\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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GamJiousyJOm8kuSJqhprrVu2k9SSpG4ZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNa3uugBJ0sLs2TfFzvFJjkzPsH7tCNu3bGTb5tFFO74BIUnnoT37ptixez8zx04AMDU9w47d+wEWLSQ8xSRJ56Gd45OvhsNJM8dOsHN8ctHew4CQpPPQkemZebUvhAEhSeeh9WtH5tW+EAaEJJ2Htm/ZyMiaVae0jaxZxfYtGxftPZyklqTz0MmJaK9ikiSdYdvm0UUNhNN5ikmS1GRASJKaDAhJUpMBIUlqMiAkSU2pqq5rWBRJjgLPd13HkF0GfKvrIpYB+2GW/TDLfnjNQvriqqpa11qxYgLiQpBkoqrGuq6ja/bDLPthlv3wmsXuC08xSZKaDAhJUpMBcX7Z1XUBy4T9MMt+mGU/vGZR+8I5CElSkyMISVKTASFJajIglpkkNyeZTHIwyb2N9f80yTNJnk7y+SRXdVHnUhigLz6UZH+Sp5L8cZJNXdQ5bHP1Q992P5ekkqzISz4H+H64K8nR3vfDU0n+QRd1Dtsg3w9J3t/7PXEgycMLfrOq8muZfAGrgK8DPwJcDHwV2HTaNj8F/MXe638E/H7XdXfYF2/oe70V+GzXdXfRD73tLgW+DHwFGOu67o6+H+4CPtJ1rcugH64B9gFv7C2/aaHv5whiebkBOFhVh6rqZeAR4Jb+Darqi1X1Um/xK8DlS1zjUhmkL77Tt/iXgJV4xcWc/dDzm8BvAd9fyuKW0KD9sNIN0g+/BDxYVX8OUFXfXOibGRDLyyjwYt/y4V7b2fwi8JmhVtSdgfoiya8k+Trw28CvLlFtS2nOfkjyY8AVVfWppSxsiQ36s/FzvdOvn0xyxdKUtqQG6Ye3AW9L8idJvpLk5oW+mQFxnkryd4AxYGfXtXSpqh6sqrcCvwb8Rtf1LLUkFwH/DvhnXdeyDPwhsKGq3gH8EfCfOq6nK6uZPc30N4E7gY8mWbuQAxkQy8sU0P9Xz+W9tlMk+Wng14GtVfWDJaptqQ3UF30eAbYNtaJuzNUPlwJ/DfhSkueAnwAeW4ET1XN+P1TVt/t+Hv4D8M4lqm0pDfJzcRh4rKqOVdU3gP/DbGDMmwGxvOwFrklydZKLgTuAx/o3SLIZeIjZcFjwucXzwCB90f9N/7eAP13C+pbKOfuhqv5fVV1WVRuqagOz81Jbq2qim3KHZpDvh7f0LW4Fnl3C+pbKnP0A7GF29ECSy5g95XRoIW+2euF1arFV1fEk9wDjzF6t8B+r6kCS+4CJqnqM2VNKlwCfSALwQlVt7azoIRmwL+7pjaaOAX8O/Hx3FQ/HgP2w4g3YD7+aZCtwHPi/zF7VtKIM2A/jwE1JngFOANur6tsLeT8ftSFJavIUkySpyYCQJDUZEJKkJgNCktRkQEiSmgwIaQkl+ViS27quQxqEASEtUGb5M6QVy29uaR6SbOg9i//jwNeAE0n+fe+5+59Psq633VuTfDbJE0n+W5If7TvMjUn+e5JD/aOJJNuT7O09bO5fL/F/TTqDASHN3zXA71bVX+0tT/RePw78q17bLuAfV9U7gQ8Dv9u3/1uAdwN/G3gAIMlNvePeAFwHvDPJjcP+j0jn4qM2pPl7vqq+0nv9CvD7vde/B+xOcgnwN3jtcSgAP9S3/56qegV4Jsmbe2039b729ZYvYTYwvjyc/4I0NwNCmr/vnWNdMTsyn66q686yTf8TeNP37/1V9dAi1CctCk8xSa/PRcDJeYQPAH/c+6S7byS5HV6dzP7rcxxnHPiF3uiDJKNJ3jSsoqVBGBDS6/M94IYkXwPeC9zXa/8g8ItJvgocYI6Px6yqzwEPA/8jyX7gk8x+1oPUGZ/mKr0OSb5bVZd0XYc0DI4gJElNjiAkSU2OICRJTQaEJKnJgJAkNRkQkqQmA0KS1PT/AXiZS0Wzh+rUAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r8eRryFl9cGf" + }, + "source": [ + "#### Choose CATE model with larger Rscore" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wTaPVNH39cGf", + "outputId": "bb85de97-45cd-4a07-d659-38bc56183bfb" + }, + "source": [ + "mdl, score = scorer.best_model([mdl for _, mdl in models])\n", + "mdl" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 57 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cULnL_mZ9cGg" + }, + "source": [ + "rootpehe_best = np.sqrt(np.mean((expected_te_val.flatten() - mdl.effect(XW_val).flatten())**2))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + }, + "id": "kLwPBbku9cGg", + "outputId": "125c9a16-1d42-4800-8ee5-c77c24d330f1" + }, + "source": [ + "plt.figure()\n", + "plt.title('RScore: {:.3f}, Root-PEHE: {:.3f}'.format(score, rootpehe_best))\n", + "plt.scatter(XW_val[:, 0], mdl.effect(XW_val), label='best')\n", + "plt.plot(XW_val[:, 0], 1 + XW_val[:, 0], 'b--', label='True effect')\n", + "plt.ylabel('Treatment Effect')\n", + "plt.xlabel('x')\n", + "plt.legend()\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AWpMuY_49cGg" + }, + "source": [ + "# 4. Interpretation" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "PkjmyQgj9cGg" + }, + "source": [ + "y, T, X, W = gen_data(2000, discrete=True)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S53DNzMd9cGg" + }, + "source": [ + "#### Fit any CATE model" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XoHzf8Lo9cGg", + "outputId": "e5b36880-2294-4fbb-d4f7-b4e2535f1d9b" + }, + "source": [ + "from econml.dml import NonParamDML\n", + "\n", + "est = NonParamDML(model_y=RandomForestRegressor(min_samples_leaf=10), # Any ML model for E[Y|X,W]\n", + " model_t=RandomForestClassifier(min_samples_leaf=10), # Any ML model for E[T|X,W]\n", + " model_final=RandomForestRegressor(max_depth=2), # Any ML model for CATE\n", + " discrete_treatment=True, # categorical or continuous treatment\n", + " cv=5, # number of crossfit folds\n", + " mc_iters=1) # repetitions of cross-fitting for stability\n", + "\n", + "est.fit(y, T, X=X, W=W, cache_values=True) # fit the CATE model" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 61 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2N5UElbB9cGh" + }, + "source": [ + "#### Interpret its behavior with a single Tree" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YnMz_1Hr9cGh", + "outputId": "cbca0155-0c49-424b-9366-c0496df4e3b5" + }, + "source": [ + "from econml.cate_interpreter import SingleTreeCateInterpreter\n", + "\n", + "intrp = SingleTreeCateInterpreter(max_depth=1)\n", + "intrp.interpret(est, X)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 62 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JVQosW2K9cGh" + }, + "source": [ + "intrp.export_graphviz(out_file='cate_tree.dot')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 248 + }, + "id": "Rvq9jg649cGh", + "outputId": "b43e7d52-09d1-45cd-b21f-08d2f51cfa55" + }, + "source": [ + "intrp.plot()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WCtJ48zI9cGi" + }, + "source": [ + "#### Make tree-based policy recommendations from CATE model" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SZL7vB1_9cGi", + "outputId": "b085ab61-7631-4959-b5e8-ed458e8e80d1" + }, + "source": [ + "from econml.cate_interpreter import SingleTreePolicyInterpreter\n", + "\n", + "intrp = SingleTreePolicyInterpreter(max_depth=1)\n", + "intrp.interpret(est, X, sample_treatment_costs=0.2)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 65 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-tWTr9ha9cGi" + }, + "source": [ + "intrp.export_graphviz(out_file='policy_tree.dot')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279 + }, + "id": "J90REc4o9cGi", + "outputId": "44988977-d39f-4fec-cca2-fe02014c6bc9" + }, + "source": [ + "intrp.plot()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YMzp0si39cGj" + }, + "source": [ + "#### Interpret CATE model with SHAP" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FS1Uy2V-9cGj" + }, + "source": [ + "shap_values = est.shap_values(X)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 194 + }, + "id": "cXdMGuBY9cGj", + "outputId": "f980abf2-c1ed-4305-f76c-48fae99331d8" + }, + "source": [ + "import shap\n", + "\n", + "# effect heterogeneity feature importances with summary plot\n", + "shap.summary_plot(shap_values['Y0']['T0_1'])" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 193 + }, + "id": "rowyWQ_l9cGj", + "outputId": "0dc74436-6ca5-417b-e45c-b260c20b6e19" + }, + "source": [ + "shap.initjs()\n", + "# explain the heterogeneity of the effect of any single sample\n", + "shap.force_plot(shap_values['Y0']['T0_1'][0])" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + " Visualization omitted, Javascript library not loaded!
\n", + " Have you run `initjs()` in this notebook? If this notebook was from another\n", + " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", + " this notebook on github the Javascript has been stripped for security. If you are using\n", + " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 70 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dA1nRbFK9cGk" + }, + "source": [ + "# 5. Validation and Sensitivity" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Nds7Fwyg9cGl" + }, + "source": [ + "y, T, Z, X, W = gen_data_iv(2000)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-zHcWkF19cGl" + }, + "source": [ + "#### Instantiate any CATE model" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0nMA97Jh9cGl" + }, + "source": [ + "from econml.iv.dml import OrthoIV\n", + "\n", + "est = OrthoIV(model_y_xw=RandomForestRegressor(), # ML model for E[Y|X,W]\n", + " model_t_xw=RandomForestRegressor(), # ML model for E[Y|X,W]\n", + " model_z_xw=RandomForestRegressor(), # ML model for E[Y|X,W]\n", + " discrete_treatment=False, # categorical/continuous treatment\n", + " discrete_instrument=False, # categorical/continuous instrument\n", + " cv=2, # number of crossfit folds\n", + " mc_iters=1) # repetitions of cross-fitting for stability" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z91lWEiQ9cGl" + }, + "source": [ + "#### Enable dowhy capabilities" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QEe9XMai9cGm" + }, + "source": [ + "import dowhy\n", + "est = est.dowhy" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BIWgh7Ce9cGm" + }, + "source": [ + "#### Then fit" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "roPf-t439cGm", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4d24cac9-58fa-44fa-87e9-f8e57ec68176" + }, + "source": [ + "est.fit(y, T, Z=Z, X=X, W=W, cache_values=True)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 74 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5tlyZMcP9cGn" + }, + "source": [ + "#### Use it as a normal EconML cate estimator" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lHr9Ooaa9cGn", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 290 + }, + "outputId": "e86a4da8-cbac-4271-ae99-8d312fd1949c" + }, + "source": [ + "est.summary()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 0.99 0.035 27.933 0.0 0.932 1.049
X1 -0.012 0.027 -0.441 0.659 -0.057 0.033
\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 1.018 0.026 38.577 0.0 0.975 1.061


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Coefficient Results \n", + "========================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "--------------------------------------------------------\n", + "X0 0.99 0.035 27.933 0.0 0.932 1.049\n", + "X1 -0.012 0.027 -0.441 0.659 -0.057 0.033\n", + " CATE Intercept Results \n", + "====================================================================\n", + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "--------------------------------------------------------------------\n", + "cate_intercept 1.018 0.026 38.577 0.0 0.975 1.061\n", + "--------------------------------------------------------------------\n", + "\n", + "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", + "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", + "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", + "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", + "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", + "\"\"\"" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 75 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "o1z0z-E79cGo", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 235 + }, + "outputId": "5cf7bc63-16c6-4eb4-9cd7-3ecd7a7128fd" + }, + "source": [ + "est.effect_inference(X[:5]).summary_frame()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " point_estimate stderr zstat pvalue ci_lower ci_upper\n", + "X \n", + "0 0.286 0.045 6.362 0.0 0.212 0.360\n", + "1 2.730 0.071 38.618 0.0 2.613 2.846\n", + "2 0.678 0.030 22.866 0.0 0.630 0.727\n", + "3 0.760 0.038 19.951 0.0 0.697 0.823\n", + "4 2.539 0.086 29.605 0.0 2.398 2.680" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 76 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cAPR5eMZ9cGo" + }, + "source": [ + "#### But now we also have DoWhy capabilities: Sensitivity Analysis" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9aUCWN489cGo" + }, + "source": [ + "ref_res = est.refute_estimate(method_name=\"add_unobserved_common_cause\",\n", + " effect_strenght_on_treatment=0.05,\n", + " effect_strength_on_outcome=0.5)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "7bTLzM_H9cGp", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7b5fdb67-e26d-46f0-b46d-572001977533" + }, + "source": [ + "print(ref_res)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Refute: Add an Unobserved Common Cause\n", + "Estimated effect:1.0314669223854354\n", + "New effect:0.9983858330683743\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MuaLIl6X9cGp" + }, + "source": [ + "# 6. Policy Learning" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Z5NSUCzK9cGp" + }, + "source": [ + "y, T, X, W = gen_data(2000, discrete=True)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_oGYT0HP9cGp" + }, + "source": [ + "#### Fit a Doubly Robust policy tree" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "PLk_-21M9cGp" + }, + "source": [ + "from econml.policy import DRPolicyTree\n", + "\n", + "est = DRPolicyTree(max_depth=2, min_impurity_decrease=0.01, honest=True)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DVBwu-oh9cGq", + "outputId": "8972c75a-1793-4611-959c-708e85ea5796" + }, + "source": [ + "est.fit(y, T, X=X, W=W)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 81 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WNpPMq0Z9cGq" + }, + "source": [ + "#### Visualize treatment policy" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279 + }, + "id": "4YnPJkJW9cGq", + "outputId": "5b9b02ea-01dd-4c3a-d598-4fb61b8e02ce" + }, + "source": [ + "est.plot()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JjBym5Iw9cGr", + "outputId": "2fdf00f7-9f6f-494e-be9a-84b85ad7d30c" + }, + "source": [ + "est.feature_importances_" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([1., 0.])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 83 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L7q7UxZG9cGr" + }, + "source": [ + "#### Produce recommended treatments" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sZ_E-Tjk9cGr", + "outputId": "4773b57f-bc7e-4f70-f65c-50ad9471041e" + }, + "source": [ + "est.predict(X[:100])" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n", + " 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n", + " 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 84 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zb3MFbCq9cGs" + }, + "source": [ + "#### Fit a Doubly Robust policy forest" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "N4CAMtPQ9cGs" + }, + "source": [ + "from econml.policy import DRPolicyForest\n", + "\n", + "est = DRPolicyForest(n_estimators=100, max_depth=2,\n", + " min_impurity_decrease=0.01, honest=True)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jK5vBCCm9cGs", + "outputId": "1b97cc54-6324-42c0-e403-fba3dc3dee14" + }, + "source": [ + "est.fit(y, T, X=X, W=W)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 86 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lhiGgCKD9cGt" + }, + "source": [ + "#### Produce recommended treatments" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lAGuJ1wB9cGt", + "outputId": "b1590aad-d1d1-4657-8e65-012ae7abec58" + }, + "source": [ + "est.predict(X[:100])" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n", + " 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 87 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3xWXtC8k9cGt" + }, + "source": [ + "#### Plot one of the trees" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279 + }, + "id": "Xen6bm7Y9cGt", + "outputId": "25f978fc-a5d0-4395-9bb2-a0b089ff2828" + }, + "source": [ + "est.plot(0)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HInWzLf-84ta" + }, + "source": [ + "#### Plot decisions as function of covariates" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "id": "0EdmyR-39cGt", + "outputId": "992dd9e8-abed-4f2b-a3cd-2d12e507ed05" + }, + "source": [ + "plt.scatter(X[:, 0], est.predict(X))\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "c90e1Sei80dv" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] } \ No newline at end of file From cde95d675472c68a80deccbee5a628832738c651 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Wed, 29 Mar 2023 18:05:55 -0700 Subject: [PATCH 02/33] update schedule --- README.md | 46 +++++++++++++++++++++++++++------------------- 1 file changed, 27 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index 7eb7a77..c47c04e 100755 --- a/README.md +++ b/README.md @@ -28,38 +28,45 @@ The tutorial assumes some basic knowledge in statistical methods, machine learni ## **Outline** -| **Title** | **Duration** | Slides | Code | -|-----------|--------------|--------|------| -| **Introduction to Causal Inference** | 20 minutes | [Slides](https://drive.google.com/file/d/1O1oVU3nX7ThzCrUxlFK-OJsxF3Bz8Khl/view?usp=sharing) | | -| **Case Studies Part 1 by CausalML** | | | | -| Introduction to CausalML| 15 minutes | [Slides](https://docs.google.com/presentation/d/1D-cqqwKyWseVoNQEH-d0TS9wqK55l3bSn3PaLFxNEWI/edit?usp=sharing) | | -| Case Study #1: Causal Impact Analysis with Observational Data: CeViChE at Uber | 30 minutes | [Slides](https://docs.google.com/presentation/d/1FvRtis2fm4c2R7XmRKWMTtZaZjUObW1fGxpNmapmjKI/edit?usp=sharing)| [Notebook](https://colab.research.google.com/drive/1ySwg9BIYWS5oLQ5haorMyiIbyiCJ431J?usp=sharing) | -| Case Study #2: Targeting Optimization: Bidder at Uber | 30 minutes |[Slides](https://drive.google.com/file/d/1QJJUCo4LH5kGQP3kaJlG1RdhjhaJWp-5/view?usp=sharing) |[Notebook](https://colab.research.google.com/drive/1fnZEHIAcNxrvSxFrlO1hRTHO7sazXbo0?usp=sharing) | -| **Case Studies Part 2 by EconML** | | | | -| Introduction to EconML| 15 minutes | [Slides](https://drive.google.com/file/d/1gt4KNznrYbwdryi9jGcC0-hDCNg7mBNE/view?usp=sharing) | [Notebook](https://colab.research.google.com/drive/1m2Ob7dc1JalEb6FIzSG1tx0qW491-YNc?usp=sharing) | -| Case Study #3: Customer Segmentation at TripAdvisor with Recommendation A/B Tests | 30 minutes | [Slides](https://drive.google.com/file/d/1yyIu_3epIVXbwzJj658Iv4vxHGjtPh8n/view?usp=sharing) | [Notebook](https://colab.research.google.com/drive/1nUhkLVpanv-gm_oA7FbValhpDpEs02wR#scrollTo=qk4_f4tx5gZz) | -| Case Study #4: Long-Term Return-on-Investment at Microsoft via Short-Term Proxies | 30 minutes | [Slides](https://drive.google.com/file/d/1FEKXFHHATntHjsEymXnEw6GAiUGMm8sG/view?usp=sharing) | [Notebook](https://colab.research.google.com/drive/1Ow7ArXRn1NJq47OLvchi26RRTdm94yv8?usp=sharing) | - -## **Keynote Speakers** - -### Yoda +| **Title** | **Duration** | Link | +|-----------|--------------|--------| +| Introduction | 10 minutes | | +| Invited Talk #1 by Yoda | 20 minutes | | +| Invited Talk #2 by Darth Vader | 20 minutes | | +| Paper #1 | 15 minutes | | +| Paper #2 | 15 minutes | | +| Paper #3 | 15 minutes | | +| Paper #4 | 15 minutes | | +| Break & Poster Session | 30 minutes | | +| Invited Talk #3 by Luke Skywalker | 20 minutes | | +| Invited Talk #4 by Princess Leia | 20 minutes | | +| Paper #1 | 15 minutes | | +| Paper #2 | 15 minutes | | +| Paper #3 | 15 minutes | | +| Paper #4 | 15 minutes | | + +## **Invited Speakers** + +### Yoda, Amazon We will give an overview of basic concepts in causal inference. A quick refresher on the main tools and terminology of causal inference: correlation vs causation, average, conditional, and individual treatment effects, causal inference via randomization, Causal inference using instrumental variables, Causal inference via unconfoundedness. -### Darth Vader +### Darth Vader, University of Darth Star We will provide an overview of CausalML, an open source Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research. We will introduce the main components of CausalML: (1) inference with causal machine learning algorithms (e.g. meta-learners, uplift trees, CEVAE, dragonnet), (2) validation/analysis methods (e.g. synthetic data generation, AUUC, sensitivity analysis, interpretability), (3) optimization methods (e.g. policy optimization, value optimization, unit selection). -### Luke Skywalker +### Luke Skywalker, Stanford University As an introductory case study for using causal inference, we will cover the use case of understanding the causal impact from observational data in the context of cross sell at Uber. We emphasize that simple comparisons of users who make cross purchase or not will produce biased estimates and that can be demonstrated in the causal inference framework. We show the use of different causal estimation methodologies through propensity score matching and meta learners to estimate the causal impact. In addition, we will use sensitivity analysis to show the robustness of the estimates. -### Princess Leia +### Princess Leia, Microsoft Research We will introduce the audience selection method with uplift modeling in online RTB, which aims to estimate heterogeneous treatment effects for advertising. It has been studied to provide a superior return on investment by selecting the most incremental users for a specific campaign. To examine the effectiveness of uplift modeling in the context of real-time bidding, we conducted the comparative analysis of four different meta-learners on real campaign data. We adapted an explore-exploit set up for offline training and online evaluation. We will also introduce how we use Targeted Maximum Likelihood Estimation (TMLE) based Average Treatment Effect (ATE) as ground truth for evaluation. ## **Accepted Papers** +To be updated + ## **Organizers** * Chu Wang, Amazon @@ -79,5 +86,6 @@ We will introduce the audience selection method with uplift modeling in online R ### EconML Team -* Keith Battocchi, Microsoft Research, EconML +* Fabio Vera, Microsoft Research, EconML * Eleanor Dillon, Microsoft Research, EconML +* Keith Battocchi, Microsoft Research, EconML From 04a772a122bfa5e73b447174caca24f232ff3d49 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Wed, 29 Mar 2023 18:12:29 -0700 Subject: [PATCH 03/33] fix CI --- _config.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/_config.yml b/_config.yml index 19a39b6..1bf87f0 100644 --- a/_config.yml +++ b/_config.yml @@ -6,8 +6,8 @@ # https://learn-the-web.algonquindesign.ca/topics/markdown-yaml-cheat-sheet/#yaml # https://learnxinyminutes.com/docs/yaml/ -title: EconML/CausalML KDD 2021 Tutorial -description: EconML/CausalML KDD 2021 Tutorial +title: KDD 2023 Workshop - Causal Inference and Machine Learning in Practice +description: KDD 2023 Workshop - Causal Inference and Machine Learning in Practice github_username: causal-machine-learning # you can comment the below line out if your repo name is not different than your baseurl github_repo: "kdd2023-workshop" @@ -39,8 +39,8 @@ baseurl: "/kdd2023-workshop" # the subpath of your site, e.g. "/blog". # Github and twitter are optional: minima: social_links: - twitter: CausalMachine - github: causal-machine-learning + - { platform: github, user_url: "https://github.com/causal-machine-learning/" } + - { platform: twitter, user_url: "https://twitter.com/CausalMachine" } # Set this to true to get LaTeX math equation support use_math: true From c60cfa25cc9ddf1c0a610973c3c6d8b0c11cb0b8 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Wed, 29 Mar 2023 18:39:57 -0700 Subject: [PATCH 04/33] revert jekyll version --- _config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_config.yml b/_config.yml index 1bf87f0..da2a486 100644 --- a/_config.yml +++ b/_config.yml @@ -100,7 +100,7 @@ plugins: paginate: 15 paginate_path: /page:num/ -remote_theme: jekyll/minima +remote_theme: jekyll/minima@6513ea8b9c1c4909b6aa79926d52a1f7d865c5e7 titles_from_headings: enabled: true From ea8b0e3c50ac1c20a9fe61d69749de2797ac7ba3 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Wed, 29 Mar 2023 18:51:04 -0700 Subject: [PATCH 05/33] remove index.html --- index.html | 120 ----------------------------------------------------- 1 file changed, 120 deletions(-) delete mode 100644 index.html diff --git a/index.html b/index.html deleted file mode 100644 index f2a19d7..0000000 --- a/index.html +++ /dev/null @@ -1,120 +0,0 @@ ---- -layout: home -search_exclude: true -image: images/logo.png ---- - -# **Causal Inference and Machine Learning in Practice with EconML and CausalML**: Industrial Use Cases at Microsoft, TripAdvisor, Uber - -## **Schedule** - -### Time - -* 4:00 AM - 7:00 AM August 15, 2021 [SGT](https://www.timeanddate.com/worldclock/converter.html?iso=20210814T200000&p1=236&p2=tz_pt&p3=tz_et) -* 4:00 PM - 7:00 PM August 14, 2021 [EDT](https://www.timeanddate.com/worldclock/converter.html?iso=20210814T200000&p1=236&p2=tz_pt&p3=tz_et) -* 1:00 PM - 4:00 PM August 14, 2021 [PDT](https://www.timeanddate.com/worldclock/converter.html?iso=20210814T200000&p1=236&p2=tz_pt&p3=tz_et) - -### Live Zoom Link - -To be shared within the KDD 21 Virtual Platform during the conference. - -## **Abstract** - -In recent years, both academic research and industry applications see an increased effort in using machine learning methods to measure granular causal effects and design optimal policies based on these causal estimates. Open source packages such as [CausalML](https://github.com/uber/causalml) and [EconML](https://github.com/microsoft/econml) provide a unified interface for applied researchers and industry practitioners with a variety of machine learning methods for causal inference. The tutorial will cover the topics including conditional treatment effect estimators by meta-learners and tree-based algorithms, model validations and sensitivity analysis, optimization algorithms including policy leaner and cost optimization. In addition, the tutorial will demonstrate the production of these algorithms in industry use cases. - -## **Target Audience and Prerequisites for the Tutorial** - -Anyone who is interested in causal inference and machine learning, especially economists/statisticians/data scientists who want to learn how to combine causal inference and machine learning with real industry use cases incorporated in large scaled machine learning systems at companies such as Microsoft, TripAdvisor and Uber. -The tutorial assumes some basic knowledge in statistical methods, machine learning algorithms and the Python programming language. - -## **Outline** - -| **Title** | **Duration** | Slides | Code | -|-----------|--------------|--------|------| -| **Introduction to Causal Inference** | 20 minutes | [Slides](https://drive.google.com/file/d/1O1oVU3nX7ThzCrUxlFK-OJsxF3Bz8Khl/view?usp=sharing) | | -| **Case Studies Part 1 by CausalML** | | | | -| Introduction to CausalML| 15 minutes | [Slides](https://drive.google.com/file/d/1ukFsX0QU0kdlQHv_VG_F8QJZtqZ86cwy/view) | | -| Case Study #1: Causal Impact Analysis with Observational Data: CeViChE at Uber | 30 minutes | [Slides](https://docs.google.com/presentation/d/1FvRtis2fm4c2R7XmRKWMTtZaZjUObW1fGxpNmapmjKI/edit?usp=sharing)| [Notebook](https://colab.research.google.com/drive/1ySwg9BIYWS5oLQ5haorMyiIbyiCJ431J?usp=sharing) | -| Case Study #2: Targeting Optimization: Bidder at Uber | 30 minutes |[Slides](https://drive.google.com/file/d/1QJJUCo4LH5kGQP3kaJlG1RdhjhaJWp-5/view?usp=sharing) |[Notebook](https://colab.research.google.com/drive/1fnZEHIAcNxrvSxFrlO1hRTHO7sazXbo0?usp=sharing) | -| **Case Studies Part 2 by EconML** | | | | -| Introduction to EconML| 15 minutes | [Slides](https://drive.google.com/file/d/1gt4KNznrYbwdryi9jGcC0-hDCNg7mBNE/view?usp=sharing) | [Notebook](https://colab.research.google.com/drive/1m2Ob7dc1JalEb6FIzSG1tx0qW491-YNc?usp=sharing) | -| Case Study #3: Customer Segmentation at TripAdvisor with Recommendation A/B Tests | 30 minutes | [Slides](https://drive.google.com/file/d/1yyIu_3epIVXbwzJj658Iv4vxHGjtPh8n/view?usp=sharing) | [Notebook](https://colab.research.google.com/drive/1nUhkLVpanv-gm_oA7FbValhpDpEs02wR#scrollTo=qk4_f4tx5gZz) | -| Case Study #4: Long-Term Return-on-Investment at Microsoft via Short-Term Proxies | 30 minutes | [Slides](https://drive.google.com/file/d/1FEKXFHHATntHjsEymXnEw6GAiUGMm8sG/view?usp=sharing) | [Notebook](https://colab.research.google.com/drive/1Ow7ArXRn1NJq47OLvchi26RRTdm94yv8?usp=sharing) | - -## **Presentation Abstracts** - -### Introduction to Causal Inference - -We will give an overview of basic concepts in causal inference. A quick refresher on the main tools and terminology of causal inference: correlation vs causation, average, conditional, and individual treatment effects, causal inference via randomization, Causal inference using instrumental variables, Causal inference via unconfoundedness. - -### Introduction to CasualML - -We will provide an overview of CausalML, an open source Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research. We will introduce the main components of CausalML: (1) inference with causal machine learning algorithms (e.g. meta-learners, uplift trees, CEVAE, dragonnet), (2) validation/analysis methods (e.g. synthetic data generation, AUUC, sensitivity analysis, interpretability), (3) optimization methods (e.g. policy optimization, value optimization, unit selection). - -### Case #1: Causal Impact Analysis with Observational Data at Uber - -As an introductory case study for using causal inference, we will cover the use case of understanding the causal impact from observational data in the context of cross sell at Uber. We emphasize that simple comparisons of users who make cross purchase or not will produce biased estimates and that can be demonstrated in the causal inference framework. We show the use of different causal estimation methodologies through propensity score matching and meta learners to estimate the causal impact. In addition, we will use sensitivity analysis to show the robustness of the estimates. - -### Case #2: Targeting Optimization: Bidder at Uber - -We will introduce the audience selection method with uplift modeling in online RTB, which aims to estimate heterogeneous treatment effects for advertising. It has been studied to provide a superior return on investment by selecting the most incremental users for a specific campaign. To examine the effectiveness of uplift modeling in the context of real-time bidding, we conducted the comparative analysis of four different meta-learners on real campaign data. We adapted an explore-exploit set up for offline training and online evaluation. We will also introduce how we use Targeted Maximum Likelihood Estimation (TMLE) based Average Treatment Effect (ATE) as ground truth for evaluation. - -### Introduction to EconML - -We will provide an overview of recent methodologies that combine machine learning with causal inference and the significant statistical power that machine learning brings to causal inference estimation methods. We will outline the structure and capabilities of the EconML package and describe some of the key causal machine learning methodologies that are implemented (e.g. double machine learning, causal forests, deepiv, doubly robust learning, dynamic double machine learning). We will also outline approaches to confidence interval construction (e.g. bootstrap, bootstrap-of-little-bags, debiased lasso), interpretability (shap values, tree interpreters) and policy learning (doubly robust policy learning). - -### Case #3: Customer Segmentation at TripAdvisor with Recommendation A/B Tests - -We examine the scenario in which we wish to learn heterogeneous treatment effects (CATE), but observational data is biased and direct experimental data (e.g. A/B test) is plagued by imperfect compliance. In this setup, TripAdvisor would like to know whether joining a membership program compels users to spend more time engaging with the website and purchasing more products. The usual approach, a direct A/B test, is infeasible: the website cannot force users to comply and become members, hence the imperfect compliance that can bias calculations. The solution is to use an alternative A/B test that was originally designed to measure whether an easier sign-up process would promote user membership. This A/B test plays the role of an instrument that nudges users to sign up for membership. We introduce EconML’s IntentToTreatDRIV estimator which can leverage this repurposed A/B test to both learn the effect of membership on user engagement and understand how these effects vary with customer features. We show how this novel methodology led to extracting key business insights and helped TripAdvisor understand and differentiate how customers engage with their platform. - -### Case #4: Long-Term Return-on-Investment at Microsoft via Short-Term Proxies - -In this case study, we talk about using observational data to measure the long term Return-on-Investment of some types of dollar value investments Microsoft gives to the enterprise customers. There are many challenges for this setting, for instance, we don't have enough period of data to identify a long term ROI, we should control the effect coming from the future investment and we are in a high dimensional data space. We then propose a surrogate based approach assuming the long-term effect is channeled through some short-term proxies and employ a dynamic adjustment to the surrogate model in order to get rid of the effect from future investment, finally apply double machine learning (DML) techniques to estimate the ROI. We apply this methodology to answer the questions like what is the average long-run ROI on each type of the investment? What types of customers have a higher ROI to a specific investment? And how different incentives impact the different solution areas. Finally we will showcase how you could use EconML to solve similar problems by only a few lines of code. - - -## **Tutors** - -### Presenters - -* Jing Pan, Uber, CausalML -* Yifeng Wu, Uber, CausalML -* Huigang Chen, Facebook, CausalML -* Totte Harinen, Toyota Research Institute, CausalML -* Paul Lo, Uber, CausalML -* Greg Lewis, Microsoft Research, EconML -* Vasilis Syrgkanis, Microsoft Research, EconML -* Miruna Oprescu, Microsoft Research, EconML -* Maggie Hei, Microsoft Research, EconML - -### Contributors - -* Jeong-Yoon Lee, Netflix Research, CausalML -* Zhenyu Zhao, Tencent, CausalML -* Keith Battocchi, Microsoft Research, EconML -* Eleanor Dillon, Microsoft Research, EconML - - -## **References** - -1. Künzel, Sören R., et al. "Metalearners for estimating heterogeneous treatment effects using machine learning." Proceedings of the national academy of sciences 116.10 (2019): 4156-4165. ([paper](https://www.pnas.org/content/pnas/116/10/4156.full.pdf)) -2. Chernozhukov, Victor, et al. "Double/debiased/neyman machine learning of treatment effects." American Economic Review 107.5 (2017): 261-65. ([paper](https://arxiv.org/pdf/1701.08687)) -3. Nie, Xinkun, and Stefan Wager. "Quasi-oracle estimation of heterogeneous treatment effects." arXiv preprint arXiv:1712.04912 (2017) ([paper](https://arxiv.org/pdf/1712.04912)) -4. Tso, Fung Po, et al. "DragonNet: a robust mobile internet service system for long-distance trains." IEEE transactions on mobile computing 12.11 (2013): 2206-2218. ([paper](https://eprints.gla.ac.uk/56409/1/56409.pdf)) -5. Louizos, Christos, et al. "Causal effect inference with deep latent-variable models." arXiv preprint arXiv:1705.08821 (2017) ([paper](https://arxiv.org/pdf/1705.08821)) -6. Wager, Stefan, and Susan Athey. "Estimation and inference of heterogeneous treatment effects using random forests." Journal of the American Statistical Association 113.523 (2018): 1228-1242. ([paper](https://www.tandfonline.com/doi/pdf/10.1080/01621459.2017.1319839)) -7. Oprescu, Miruna, et al. "EconML: A Machine Learning Library for Estimating Heterogeneous Treatment Effects." ([repo](https://github.com/microsoft/EconML)) -8. Chen, Huigang, et al. "Causalml: Python package for causal machine learning." arXiv preprint arXiv:2002.11631 (2020) ([repo](https://github.com/uber/causalml)) -9. Yao, Liuyi, et al. "A survey on causal inference." arXiv preprint arXiv:2002.02770 (2020). ([paper](https://arxiv.org/pdf/2002.02770.pdf)) -10. Goldenberg, Dmitri, et al. "Personalization in Practice: Methods and Applications." Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 2021 ([paper](https://drive.google.com/drive/folders/1c_khoTDRbkoRY5OiaxEfUxRQkyNv3FeK)) -11. Blackwell, Matthew. "A selection bias approach to sensitivity analysis for causal effects." Political Analysis 22.2 (2014): 169-182. ([paper](https://www.cambridge.org/core/journals/political-analysis/article/selection-bias-approach-to-sensitivity-analysis-for-causal-effects/788C169FAF5482452566811136D4F9B4)) -12. Athey, Susan, and Stefan Wager. "Efficient policy learning." arXiv preprint arXiv:1702.02896 (2017). ([paper](https://arxiv.org/pdf/1702.02896.pdf)) -13. Sharma, Amit, and Emre Kiciman. "Causal Inference and Counterfactual Reasoning." Proceedings of the 7th ACM IKDD CoDS and 25th COMAD. 2020. 369-370. ([paper](https://dl.acm.org/doi/abs/10.1145/3371158.3371231)) -14. Li, Ang, and Judea Pearl. "Unit selection based on counterfactual logic." Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. 2019 ([paper](https://par.nsf.gov/biblio/10180278)) -15. Kennedy, Edward H. "Optimal doubly robust estimation of heterogeneous causal effects." arXiv preprint arXiv:2004.14497 (2020) ([paper](https://arxiv.org/pdf/2004.14497.pdf)) -16. Gruber, Susan, and Mark J. Van Der Laan. "Targeted maximum likelihood estimation: A gentle introduction." (2009) ([paper](https://biostats.bepress.com/cgi/viewcontent.cgi?article=1255&context=ucbbiostat)) -17. D. Foster, V. Syrgkanis. Orthogonal Statistical Learning. Proceedings of the 32nd Annual Conference on Learning Theory (COLT), 2019 ([paper](https://arxiv.org/pdf/1901.09036.pdf)) -18. V. Syrgkanis, V. Lei, M. Oprescu, M. Hei, K. Battocchi, G. Lewis. Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments. Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS), 2019 ([paper](https://arxiv.org/pdf/1905.10176.pdf)) -19. M. Oprescu, V. Syrgkanis and Z. S. Wu. Orthogonal Random Forest for Causal Inference. Proceedings of the 36th International Conference on Machine Learning (ICML), 2019 ([paper](http://proceedings.mlr.press/v97/oprescu19a/oprescu19a.pdf)) -20. Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. Deep IV: A flexible approach for counterfactual prediction. Proceedings of the 34th International Conference on Machine Learning, ICML'17, 2017 ([paper](http://proceedings.mlr.press/v70/hartford17a/hartford17a.pdf)) -21. Battocchi, K., Dillon, E., Hei, M., Lewis, G., Oprescu, M., & Syrgkanis, V. (2021). Estimating the Long-Term Effects of Novel Treatments. arXiv preprint arXiv:2103.08390. ([paper](https://arxiv.org/pdf/2103.08390.pdf)) -22. Lewis, G., & Syrgkanis, V. (2020). Double/Debiased Machine Learning for Dynamic Treatment Effects. arXiv preprint arXiv:2002.07285. ([paper](https://arxiv.org/pdf/2002.07285.pdf)) From 027f286fab7bdcb593d1182847c56aed65390bca Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Wed, 29 Mar 2023 18:59:42 -0700 Subject: [PATCH 06/33] add index.html back --- index.html | 117 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 117 insertions(+) create mode 100644 index.html diff --git a/index.html b/index.html new file mode 100644 index 0000000..223db4b --- /dev/null +++ b/index.html @@ -0,0 +1,117 @@ +--- +layout: home +search_exclude: true +image: images/logo.png +--- + +# **Causal Inference and Machine Learning in Practice**: Use cases for Product, Brand, Policy and Beyond + +## **Schedule** + +* Long Beach Convention & Entertainment Center, 300 E Ocean Blvd, Long Beach, CA 90802 ([Google +Map](https://goo.gl/maps/1N3XGEovGgJqXAV98)) +* 9:00 AM - 1:00 PM August 7, 2023 [PDT] + +## **Abstract** + +In recent years, both academic research and industry applications see an increased effort in using machine learning +methods to measure granular causal effects and design optimal policies based on these causal estimates. Open source +packages such as [CausalML](https://github.com/uber/causalml) and [EconML](https://github.com/microsoft/econml) provide +a unified interface for applied researchers and industry practitioners with a variety of machine learning methods for +causal inference. The tutorial will cover the topics including conditional treatment effect estimators by meta-learners +and tree-based algorithms, model validations and sensitivity analysis, optimization algorithms including policy leaner +and cost optimization. In addition, the tutorial will demonstrate the production of these algorithms in industry use +cases. + +## **Target Audience for the Workshop** + +Anyone who is interested in causal inference and machine learning, especially economists/statisticians/data scientists +who want to learn how to combine causal inference and machine learning with real industry use cases incorporated in +large scaled machine learning systems at companies such as Microsoft, TripAdvisor and Uber. +The tutorial assumes some basic knowledge in statistical methods, machine learning algorithms and the Python programming +language. + +## **Important Dates** + +* May 23, 2023 [AoE]: Workshop paper submission deadline +* June 23, 2023: Paper decision notifications +* August 7, 2023: Workshop + +## **Outline** + +| **Title** | **Duration** | Link | +|-----------|--------------|--------| +| Introduction | 10 minutes | | +| Invited Talk #1 by Yoda | 20 minutes | | +| Invited Talk #2 by Darth Vader | 20 minutes | | +| Paper #1 | 15 minutes | | +| Paper #2 | 15 minutes | | +| Paper #3 | 15 minutes | | +| Paper #4 | 15 minutes | | +| Break & Poster Session | 30 minutes | | +| Invited Talk #3 by Luke Skywalker | 20 minutes | | +| Invited Talk #4 by Princess Leia | 20 minutes | | +| Paper #1 | 15 minutes | | +| Paper #2 | 15 minutes | | +| Paper #3 | 15 minutes | | +| Paper #4 | 15 minutes | | + +## **Invited Speakers** + +### Yoda, Amazon + +We will give an overview of basic concepts in causal inference. A quick refresher on the main tools and terminology of +causal inference: correlation vs causation, average, conditional, and individual treatment effects, causal inference via +randomization, Causal inference using instrumental variables, Causal inference via unconfoundedness. + +### Darth Vader, University of Darth Star + +We will provide an overview of CausalML, an open source Python package that provides a suite of uplift modeling and +causal inference methods using machine learning algorithms based on recent research. We will introduce the main +components of CausalML: (1) inference with causal machine learning algorithms (e.g. meta-learners, uplift trees, CEVAE, +dragonnet), (2) validation/analysis methods (e.g. synthetic data generation, AUUC, sensitivity analysis, +interpretability), (3) optimization methods (e.g. policy optimization, value optimization, unit selection). + +### Luke Skywalker, Stanford University + +As an introductory case study for using causal inference, we will cover the use case of understanding the causal impact +from observational data in the context of cross sell at Uber. We emphasize that simple comparisons of users who make +cross purchase or not will produce biased estimates and that can be demonstrated in the causal inference framework. We +show the use of different causal estimation methodologies through propensity score matching and meta learners to +estimate the causal impact. In addition, we will use sensitivity analysis to show the robustness of the estimates. + +### Princess Leia, Microsoft Research + +We will introduce the audience selection method with uplift modeling in online RTB, which aims to estimate heterogeneous +treatment effects for advertising. It has been studied to provide a superior return on investment by selecting the most +incremental users for a specific campaign. To examine the effectiveness of uplift modeling in the context of real-time +bidding, we conducted the comparative analysis of four different meta-learners on real campaign data. We adapted an +explore-exploit set up for offline training and online evaluation. We will also introduce how we use Targeted Maximum +Likelihood Estimation (TMLE) based Average Treatment Effect (ATE) as ground truth for evaluation. + +## **Accepted Papers** + +To be updated + +## **Organizers** + +* Chu Wang, Amazon +* Yingfei Wang, University of Washington +* Xinwei Ma, UC San Diego +* [Zeyu Zheng](mailto:zyzheng@berkeley.edu), UC Berkeley, Amazon - main contact + +### CausalML Team + +* Jing Pan, Snap, CausalML +* Yifeng Wu, Uber, CausalML +* Huigang Chen, Meta, CausalML +* Totte Harinen, AirBnB, CausalML +* Paul Lo, Snap, CausalML +* [Jeong-Yoon Lee](mailto:jeong@uber.com), Uber, CausalML - main contact +* Zhenyu Zhao, Tencent, CausalML + +### EconML Team + +* Fabio Vera, Microsoft Research, EconML +* Eleanor Dillon, Microsoft Research, EconML +* Keith Battocchi, Microsoft Research, EconML \ No newline at end of file From 6e2731b5a8772d40fb390b7a04ec9b9791d4f28a Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Fri, 7 Apr 2023 18:16:49 -0700 Subject: [PATCH 07/33] Updated invited speakers --- index.html | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/index.html b/index.html index 223db4b..0cf0096 100644 --- a/index.html +++ b/index.html @@ -42,8 +42,8 @@ | **Title** | **Duration** | Link | |-----------|--------------|--------| | Introduction | 10 minutes | | -| Invited Talk #1 by Yoda | 20 minutes | | -| Invited Talk #2 by Darth Vader | 20 minutes | | +| Invited Talk #1 by Raif Rustamov | 20 minutes | | +| Invited Talk #2 by Ruomeng Cui | 20 minutes | | | Paper #1 | 15 minutes | | | Paper #2 | 15 minutes | | | Paper #3 | 15 minutes | | @@ -58,13 +58,11 @@ ## **Invited Speakers** -### Yoda, Amazon +### Raif Rustamov, Amazon -We will give an overview of basic concepts in causal inference. A quick refresher on the main tools and terminology of -causal inference: correlation vs causation, average, conditional, and individual treatment effects, causal inference via -randomization, Causal inference using instrumental variables, Causal inference via unconfoundedness. +Video creatives have a substantial impact on consumer experiences and brand perceptions, but evaluating their effect on shopper behavior remains a complex challenge. In this talk, we introduce the Creative Optimality Gap (COG), a novel metric developed using causal-inferential machine learning methodologies to quantify the relative optimality of video creatives. We provide an example application of this approach in assessing the effectiveness of video creatives for brand advertising at Amazon. -### Darth Vader, University of Darth Star +### Ruomeng Cui, Emory University We will provide an overview of CausalML, an open source Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research. We will introduce the main @@ -114,4 +112,4 @@ * Fabio Vera, Microsoft Research, EconML * Eleanor Dillon, Microsoft Research, EconML -* Keith Battocchi, Microsoft Research, EconML \ No newline at end of file +* Keith Battocchi, Microsoft Research, EconML From 824bc5475daa9cf4e6963a8dc8ce5c1d34910ce1 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Fri, 7 Apr 2023 18:18:08 -0700 Subject: [PATCH 08/33] Update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index c47c04e..ed0b994 100755 --- a/README.md +++ b/README.md @@ -31,8 +31,8 @@ The tutorial assumes some basic knowledge in statistical methods, machine learni | **Title** | **Duration** | Link | |-----------|--------------|--------| | Introduction | 10 minutes | | -| Invited Talk #1 by Yoda | 20 minutes | | -| Invited Talk #2 by Darth Vader | 20 minutes | | +| Invited Talk #1 by Raif Rustamov | 20 minutes | | +| Invited Talk #2 by Ruomeng Cui | 20 minutes | | | Paper #1 | 15 minutes | | | Paper #2 | 15 minutes | | | Paper #3 | 15 minutes | | @@ -47,11 +47,11 @@ The tutorial assumes some basic knowledge in statistical methods, machine learni ## **Invited Speakers** -### Yoda, Amazon +### Raif Rustamov, Amazon -We will give an overview of basic concepts in causal inference. A quick refresher on the main tools and terminology of causal inference: correlation vs causation, average, conditional, and individual treatment effects, causal inference via randomization, Causal inference using instrumental variables, Causal inference via unconfoundedness. +Video creatives have a substantial impact on consumer experiences and brand perceptions, but evaluating their effect on shopper behavior remains a complex challenge. In this talk, we introduce the Creative Optimality Gap (COG), a novel metric developed using causal-inferential machine learning methodologies to quantify the relative optimality of video creatives. We provide an example application of this approach in assessing the effectiveness of video creatives for brand advertising at Amazon. -### Darth Vader, University of Darth Star +### Ruomeng Cui, Emory University We will provide an overview of CausalML, an open source Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research. We will introduce the main components of CausalML: (1) inference with causal machine learning algorithms (e.g. meta-learners, uplift trees, CEVAE, dragonnet), (2) validation/analysis methods (e.g. synthetic data generation, AUUC, sensitivity analysis, interpretability), (3) optimization methods (e.g. policy optimization, value optimization, unit selection). From 3422b6473304867dd6c49058fc425246aea7a64c Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Tue, 11 Apr 2023 16:15:32 -0700 Subject: [PATCH 09/33] add Ang Li to invited speakers --- README.md | 32 +- index.html | 39 +- notebooks/kdd_intro_to_EconML.ipynb | 3473 --------------------------- 3 files changed, 41 insertions(+), 3503 deletions(-) delete mode 100644 notebooks/kdd_intro_to_EconML.ipynb diff --git a/README.md b/README.md index ed0b994..77b6824 100755 --- a/README.md +++ b/README.md @@ -38,8 +38,8 @@ The tutorial assumes some basic knowledge in statistical methods, machine learni | Paper #3 | 15 minutes | | | Paper #4 | 15 minutes | | | Break & Poster Session | 30 minutes | | -| Invited Talk #3 by Luke Skywalker | 20 minutes | | -| Invited Talk #4 by Princess Leia | 20 minutes | | +| Invited Talk #3 by Ang Li | 20 minutes | | +| Invited Talk #4 by TBD | 20 minutes | | | Paper #1 | 15 minutes | | | Paper #2 | 15 minutes | | | Paper #3 | 15 minutes | | @@ -53,15 +53,25 @@ Video creatives have a substantial impact on consumer experiences and brand perc ### Ruomeng Cui, Emory University -We will provide an overview of CausalML, an open source Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research. We will introduce the main components of CausalML: (1) inference with causal machine learning algorithms (e.g. meta-learners, uplift trees, CEVAE, dragonnet), (2) validation/analysis methods (e.g. synthetic data generation, AUUC, sensitivity analysis, interpretability), (3) optimization methods (e.g. policy optimization, value optimization, unit selection). - -### Luke Skywalker, Stanford University - -As an introductory case study for using causal inference, we will cover the use case of understanding the causal impact from observational data in the context of cross sell at Uber. We emphasize that simple comparisons of users who make cross purchase or not will produce biased estimates and that can be demonstrated in the causal inference framework. We show the use of different causal estimation methodologies through propensity score matching and meta learners to estimate the causal impact. In addition, we will use sensitivity analysis to show the robustness of the estimates. - -### Princess Leia, Microsoft Research - -We will introduce the audience selection method with uplift modeling in online RTB, which aims to estimate heterogeneous treatment effects for advertising. It has been studied to provide a superior return on investment by selecting the most incremental users for a specific campaign. To examine the effectiveness of uplift modeling in the context of real-time bidding, we conducted the comparative analysis of four different meta-learners on real campaign data. We adapted an explore-exploit set up for offline training and online evaluation. We will also introduce how we use Targeted Maximum Likelihood Estimation (TMLE) based Average Treatment Effect (ATE) as ground truth for evaluation. +We will provide an overview of CausalML, an open source Python package that provides a suite of uplift modeling and +causal inference methods using machine learning algorithms based on recent research. We will introduce the main +components of CausalML: (1) inference with causal machine learning algorithms (e.g. meta-learners, uplift trees, CEVAE, +dragonnet), (2) validation/analysis methods (e.g. synthetic data generation, AUUC, sensitivity analysis, +interpretability), (3) optimization methods (e.g. policy optimization, value optimization, unit selection). + +### Ang Li, University of California, Los Angeles + +The unit selection problem aims to identify a set of individuals who are most likely to +exhibit a desired mode of behavior, which is defined in counterfactual terms. A typical +example is that of selecting individuals who would respond one way if encouraged and a +different way if not encouraged. Unlike previous works on this problem, which rely on ad-hoc +heuristics, we approach this problem formally, using counterfactual logic, to properly capture +the nature of the desired behavior. This formalism enables us to derive an informative +selection criterion which integrates experimental and observational data. We show that a +more accurate selection criterion can be achieved when structural information is available +in the form of a causal diagram. We further discuss data availability issue regarding the +derivation of the selection criterion without the observational or experimental data. We +demonstrate the superiority of this criterion over A/B-test-based approaches. ## **Accepted Papers** diff --git a/index.html b/index.html index 0cf0096..2610784 100644 --- a/index.html +++ b/index.html @@ -49,8 +49,8 @@ | Paper #3 | 15 minutes | | | Paper #4 | 15 minutes | | | Break & Poster Session | 30 minutes | | -| Invited Talk #3 by Luke Skywalker | 20 minutes | | -| Invited Talk #4 by Princess Leia | 20 minutes | | +| Invited Talk #3 by Ang Li | 20 minutes | | +| Invited Talk #4 by TBD | 20 minutes | | | Paper #1 | 15 minutes | | | Paper #2 | 15 minutes | | | Paper #3 | 15 minutes | | @@ -60,7 +60,11 @@ ### Raif Rustamov, Amazon -Video creatives have a substantial impact on consumer experiences and brand perceptions, but evaluating their effect on shopper behavior remains a complex challenge. In this talk, we introduce the Creative Optimality Gap (COG), a novel metric developed using causal-inferential machine learning methodologies to quantify the relative optimality of video creatives. We provide an example application of this approach in assessing the effectiveness of video creatives for brand advertising at Amazon. +Video creatives have a substantial impact on consumer experiences and brand perceptions, but evaluating their effect on +shopper behavior remains a complex challenge. In this talk, we introduce the Creative Optimality Gap (COG), a novel +metric developed using causal-inferential machine learning methodologies to quantify the relative optimality of video +creatives. We provide an example application of this approach in assessing the effectiveness of video creatives for +brand advertising at Amazon. ### Ruomeng Cui, Emory University @@ -70,22 +74,19 @@ dragonnet), (2) validation/analysis methods (e.g. synthetic data generation, AUUC, sensitivity analysis, interpretability), (3) optimization methods (e.g. policy optimization, value optimization, unit selection). -### Luke Skywalker, Stanford University +### Ang Li, University of California, Los Angeles -As an introductory case study for using causal inference, we will cover the use case of understanding the causal impact -from observational data in the context of cross sell at Uber. We emphasize that simple comparisons of users who make -cross purchase or not will produce biased estimates and that can be demonstrated in the causal inference framework. We -show the use of different causal estimation methodologies through propensity score matching and meta learners to -estimate the causal impact. In addition, we will use sensitivity analysis to show the robustness of the estimates. - -### Princess Leia, Microsoft Research - -We will introduce the audience selection method with uplift modeling in online RTB, which aims to estimate heterogeneous -treatment effects for advertising. It has been studied to provide a superior return on investment by selecting the most -incremental users for a specific campaign. To examine the effectiveness of uplift modeling in the context of real-time -bidding, we conducted the comparative analysis of four different meta-learners on real campaign data. We adapted an -explore-exploit set up for offline training and online evaluation. We will also introduce how we use Targeted Maximum -Likelihood Estimation (TMLE) based Average Treatment Effect (ATE) as ground truth for evaluation. +The unit selection problem aims to identify a set of individuals who are most likely to +exhibit a desired mode of behavior, which is defined in counterfactual terms. A typical +example is that of selecting individuals who would respond one way if encouraged and a +different way if not encouraged. Unlike previous works on this problem, which rely on ad-hoc +heuristics, we approach this problem formally, using counterfactual logic, to properly capture +the nature of the desired behavior. This formalism enables us to derive an informative +selection criterion which integrates experimental and observational data. We show that a +more accurate selection criterion can be achieved when structural information is available +in the form of a causal diagram. We further discuss data availability issue regarding the +derivation of the selection criterion without the observational or experimental data. We +demonstrate the superiority of this criterion over A/B-test-based approaches. ## **Accepted Papers** @@ -112,4 +113,4 @@ * Fabio Vera, Microsoft Research, EconML * Eleanor Dillon, Microsoft Research, EconML -* Keith Battocchi, Microsoft Research, EconML +* Keith Battocchi, Microsoft Research, EconML \ No newline at end of file diff --git a/notebooks/kdd_intro_to_EconML.ipynb b/notebooks/kdd_intro_to_EconML.ipynb deleted file mode 100644 index defca44..0000000 --- a/notebooks/kdd_intro_to_EconML.ipynb +++ /dev/null @@ -1,3473 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - }, - "colab": { - "name": 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"![EconML-Logo-MSFT-color.png](data:image/png;base64,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)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jYAHRpIF-BRW" - }, - "source": [ - "# **KDD2021 Tutorial:** [Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber](https://causal-machine-learning.github.io/kdd2023-workshop/)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AYlHqLQf-PjP" - }, - "source": [ - "# Introduction to [EconML](https://github.com/microsoft/EconML)\n", - "\n", - "A python library for estimation of heterogeneous treatment effects with Machine Learning.\n", - "\n", - "**Presentation:** [Introduction to EconML](https://drive.google.com/file/d/1gt4KNznrYbwdryi9jGcC0-hDCNg7mBNE/view?usp=sharing)\n", - "\n", - "**Github:** https://github.com/microsoft/EconML\n", - "\n", - "**Documentation:** https://econml.azurewebsites.net/\n", - "\n", - "By the Microsoft Research project [ALICE (Automated Learning and Intelligence for Causation and Economics)](https://www.microsoft.com/en-us/research/project/alice/)" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "cEqRBSJV9dTX" - }, - "source": [ - "#!pip install econml" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "yPw9zWWm3H9e" - }, - "source": [ - "!pip install git+https://github.com/microsoft/EconML.git@mehei/driv#egg=econml" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "PKxsq0Mv9cGD" - }, - "source": [ - "import numpy as np\n", - "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", - "from sklearn.linear_model import LassoCV, Lasso\n", - "from sklearn.preprocessing import PolynomialFeatures\n", - "from sklearn.model_selection import train_test_split\n", - "import matplotlib.pyplot as plt\n", - "import scipy\n", - "import warnings\n", - "warnings.simplefilter('ignore')" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "WNvGZiKF9cGG" - }, - "source": [ - "def gen_data(n, discrete=False):\n", - " X = np.random.normal(0, 1, size=(n, 2))\n", - " W = np.random.normal(0, 1, size=(n, 2))\n", - " if discrete:\n", - " T = np.random.binomial(1, scipy.special.expit(W[:, 0]))\n", - " else:\n", - " T = W[:, 0] + np.random.normal(0, 1, size=(n,))\n", - " y = (X[:, 0] + 1) * T + W[:, 0] + np.random.normal(0, 1, size=(n,))\n", - " return y, T, X, W\n", - "\n", - "def gen_data_iv(n):\n", - " X = np.random.normal(0, 1, size=(n, 2))\n", - " W = np.random.normal(0, 1, size=(n, 2))\n", - " U = np.random.normal(0, 1, size=(n,))\n", - " Z = np.random.normal(0, 1, size=(n,))\n", - " T = Z + W[:, 0] + U\n", - " y = (X[:, 0] + 1) * T + W[:, 0] + U\n", - " return y, T, Z, X, W" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7xzAqDMb9cGH" - }, - "source": [ - "# 1. Estimation under Exogeneity" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "TuRauvKA9cGH" - }, - "source": [ - "y, T, X, W = gen_data(1000)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nHhjI8Kb9cGI", - "outputId": "059fd233-bd8e-4de3-fa61-1a042b381aba" - }, - "source": [ - "from econml.dml import NonParamDML\n", - "\n", - "est = NonParamDML(model_y=RandomForestRegressor(), # Any ML model for E[Y|X,W]\n", - " model_t=RandomForestRegressor(), # Any ML model for E[T|X,W]\n", - " model_final=RandomForestRegressor(max_depth=2), # Any ML model for CATE\n", - " discrete_treatment=False, # categorical or continuous treatment\n", - " cv=2, # number of crossfit folds\n", - " mc_iters=1) # repetitions of cross-fitting for stability\n", - "\n", - "est.fit(y, T, X=X, W=W, cache_values=True) # fit the CATE model" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 6 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Gc4fpcMr9cGJ" - }, - "source": [ - "#### Personalized effect estimates on test samples" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9rqqLxnt9cGJ", - "outputId": "cd71c483-e9e1-48f8-fab7-e8534777a008" - }, - "source": [ - "# personalized effect for each sample from going from treatment 0 to treatment level 1\n", - "est.effect(X[:5], T0=0, T1=1)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([ 1.54592291, 0.87946172, -0.47128723, 0.81285758, 3.1442986 ])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 7 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qC23J2R09cGJ" - }, - "source": [ - "#### ML model diagnostics" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "yE2v-YzX9cGK", - "outputId": "6d074083-9a40-4222-d7bb-f2d158fc2df0" - }, - "source": [ - "# fitted nuisance models for each cross-fitting fold and out-of-sample scores\n", - "est.models_y, est.nuisance_scores_y" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "([[RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False),\n", - " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False)]],\n", - " [[0.5409335662361056, 0.48599679070328405]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 8 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JXOd9Rfa9cGK", - "outputId": "dfbebf0a-19e9-4f15-9182-e22318015360" - }, - "source": [ - "est.models_t, est.nuisance_scores_t" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "([[RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False),\n", - " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False)]],\n", - " [[0.4282760371961908, 0.3782367983417111]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 9 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CLbqvUvr9cGK" - }, - "source": [ - "#### CATE model diagnostics" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IdiMMpsQ9cGL", - "outputId": "6b6277ca-32b3-4d29-f5f4-37b765551e39" - }, - "source": [ - "# in-sample goodness-of-fit score for the final cate model\n", - "print(est.score_)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "1.4717907756757855\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "968ktBK_9cGL" - }, - "source": [ - "#### Nuisance quantity diagnostics" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "KDwBlm-i9cGL" - }, - "source": [ - "# calculated residuals for each training sample\n", - "yres, Tres, X_cache, W_cache = est.residuals_" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Jkc1WSi19cGL" - }, - "source": [ - "# 2. Estimation with Instruments" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "8pjyjlRQ9cGM" - }, - "source": [ - "y, T, Z, X, W = gen_data_iv(2000)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "l5gr5sGS9cGM" - }, - "source": [ - "from econml.iv.dml import OrthoIV\n", - "\n", - "est = OrthoIV(model_y_xw=RandomForestRegressor(), # ML model for E[Y|X,W]\n", - " model_t_xw=RandomForestRegressor(), # ML model for E[T|X,W]\n", - " model_z_xw=RandomForestRegressor(), # ML model for E[Z|X,W]\n", - " discrete_treatment=False, # categorical/continuous treatment\n", - " discrete_instrument=False, # categorical/continuous instrument\n", - " cv=2, # number of crossfit folds\n", - " mc_iters=1) # repetitions of cross-fitting for stability" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "b_k1NpEp9cGM", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "8b057fc5-bd87-43fa-ea75-33ee736a2a88" - }, - "source": [ - "est.fit(y, T, Z=Z, X=X, W=W, cache_values=True)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 14 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "a0zr3m3r9cGM" - }, - "source": [ - "#### Personalized effect estimates on test samples" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "2wAnhhq99cGN", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "2061468c-6969-4b1a-86fa-ba16a8f6b49c" - }, - "source": [ - "est.effect(X, T0=0, T1=1)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([-0.32390668, 2.23728003, 0.70346148, ..., 0.85984079,\n", - " 0.17539819, 1.07325619])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 15 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "D9ESfbo29cGN" - }, - "source": [ - "#### ML model diagnostics" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "9JlbW4an9cGN", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "f77e36a8-295f-4dff-e11d-21057357e4c8" - }, - "source": [ - "est.models_y_xw, est.nuisance_scores_y_xw" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "([[RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False),\n", - " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False)]],\n", - " [[0.3493220983540998, 0.2621905464991312]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 16 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "e1YCQWZK9cGO", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "71292840-3f77-4985-fdf3-855ad7d4f07a" - }, - "source": [ - "est.models_t_xw, est.nuisance_scores_t_xw" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "([[RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False),\n", - " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False)]],\n", - " [[0.24243204274642238, 0.22952337252664082]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 17 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "cN5Ga_YH9cGO", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "fca11455-0e2c-4881-96a9-0e07a67d4461" - }, - "source": [ - "est.models_z_xw, est.nuisance_scores_z_xw" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "([[RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False),\n", - " RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n", - " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", - " max_samples=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, n_jobs=None, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False)]],\n", - " [[-0.10057707531258632, -0.08476759034439452]])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 18 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oNkC5W9i9cGP" - }, - "source": [ - "#### CATE model diagnostics" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "zENiyXuV9cGP", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "621aba3d-e433-4c89-e0dc-1879afd565f6" - }, - "source": [ - "print(est.score_)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "5.018208071305707e-16\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "slJuNhWU9cGP" - }, - "source": [ - "#### Nuisance quantity diagnostics" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "1o5aP05y9cGP" - }, - "source": [ - "yres, Tres, Zres, Xc, Wc, Zc = est.residuals_" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WrsMl9pm9cGQ" - }, - "source": [ - "# 3. Inference" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "oeTIUL6_9cGQ" - }, - "source": [ - "y, T, X, W = gen_data(1000)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "K0kinaK__Djv" - }, - "source": [ - "### Generic Bootstrap Inference" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "rSGauZ1k9cGQ" - }, - "source": [ - "from econml.dml import NonParamDML\n", - "from econml.sklearn_extensions.linear_model import WeightedLasso\n", - "\n", - "est = NonParamDML(model_y=Lasso(alpha=.1), # Any ML model for E[Y|X,W]\n", - " model_t=Lasso(alpha=.1), # Any ML model for E[T|X,W]\n", - " model_final=WeightedLasso(alpha=.1), # Any ML model for CATE that accepts `sample_weight` at fit\n", - " discrete_treatment=False, # categorical or continuous treatment\n", - " cv=2, # number of crossfit folds\n", - " mc_iters=1) # repetitions of cross-fitting for stability" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "lckeXBSV9cGR", - "outputId": "d1cecd80-0022-4e16-c4a9-dc1081f474f3" - }, - "source": [ - "est.fit(y, T, X=X, W=W, inference='bootstrap') # fit the CATE model" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 23 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 235 - }, - "id": "p-RP37i-9cGR", - "outputId": "2126735a-91d6-4908-eb8a-b36bcae1cbc8" - }, - "source": [ - "est.effect_inference(X[:5], T0=0, T1=1).summary_frame()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
01.2700.03932.6200.01.2101.334
11.7070.05133.6340.01.6311.787
22.4170.07930.4280.02.3042.559
31.3740.04133.7000.01.3121.445
4-0.3020.070-4.3380.0-0.449-0.216
\n", - "
" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "0 1.270 0.039 32.620 0.0 1.210 1.334\n", - "1 1.707 0.051 33.634 0.0 1.631 1.787\n", - "2 2.417 0.079 30.428 0.0 2.304 2.559\n", - "3 1.374 0.041 33.700 0.0 1.312 1.445\n", - "4 -0.302 0.070 -4.338 0.0 -0.449 -0.216" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 24 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 251 - }, - "id": "9tGAONKF9cGS", - "outputId": "cce9a4d0-d2a9-4991-87aa-90504695a8a9" - }, - "source": [ - "est.ate_inference(X)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Uncertainty of Mean Point Estimate
mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper
0.989 0.061 16.1 0.0 0.888 1.09
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Distribution of Point Estimate
std_point pct_point_lower pct_point_upper
1.041 -0.704 2.734
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Total Variance of Point Estimate
stderr_point ci_point_lower ci_point_upper
1.042 -0.698 2.737


Note: The stderr_mean is a conservative upper bound." - ], - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 25 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ab22GTTm9cGS", - "outputId": "b26f4c08-1938-43fb-c81b-36ca177254f8" - }, - "source": [ - "from econml.inference import BootstrapInference\n", - "est.fit(y, T, X=X, W=W,\n", - " inference=BootstrapInference(n_bootstrap_samples=100,\n", - " bootstrap_type='normal'))" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 26 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 235 - }, - "id": "MK02FTmf9cGS", - "outputId": "3d1bb9f4-cbcf-45ac-930b-f6d837b407b0" - }, - "source": [ - "est.effect_inference(X[:5], T0=0, T1=1).summary_frame()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
01.2670.04627.5910.01.1921.343
11.7040.05530.9780.01.6131.794
22.4140.07930.6230.02.2842.543
31.3720.04828.8790.01.2941.450
4-0.3030.073-4.1250.0-0.424-0.182
\n", - "
" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "0 1.267 0.046 27.591 0.0 1.192 1.343\n", - "1 1.704 0.055 30.978 0.0 1.613 1.794\n", - "2 2.414 0.079 30.623 0.0 2.284 2.543\n", - "3 1.372 0.048 28.879 0.0 1.294 1.450\n", - "4 -0.303 0.073 -4.125 0.0 -0.424 -0.182" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 27 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 251 - }, - "id": "qIKkO36S9cGT", - "outputId": "574a2f94-2cca-463f-adf6-b2193d1d0e6c" - }, - "source": [ - "est.ate_inference(X)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Uncertainty of Mean Point Estimate
mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper
0.986 0.065 15.226 0.0 0.88 1.093
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Distribution of Point Estimate
std_point pct_point_lower pct_point_upper
1.039 -0.704 2.73
\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Total Variance of Point Estimate
stderr_point ci_point_lower ci_point_upper
1.041 -0.703 2.735


Note: The stderr_mean is a conservative upper bound." - ], - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 28 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LH0Uo_yJ--G_" - }, - "source": [ - "### Tailored Valid Inference" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZfUSZoW-4TJQ" - }, - "source": [ - "#### Heteroskedasticity-robust OLS inference for linear CATE models $\\theta(x)=\\langle\\theta, \\phi(x)\\rangle$" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "uPiPxMU19cGT" - }, - "source": [ - "from econml.dml import LinearDML\n", - "\n", - "est = LinearDML(model_y=RandomForestRegressor(), # Any ML model for E[Y|X,W]\n", - " model_t=RandomForestRegressor(), # Any ML model for E[T|X,W]\n", - " featurizer=PolynomialFeatures(degree=2, include_bias=False)) # any featurizer for " - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "qAQKnPA59cGU", - "outputId": "77583b37-6895-4c2e-c636-d948a6de8798" - }, - "source": [ - "est.fit(y, T, X=X, W=W)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 30 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 353 - }, - "id": "Cy73CEcm9cGU", - "outputId": "749cefb2-54d6-475a-d630-4a8d9beba4f6" - }, - "source": [ - "est.summary()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 1.001 0.034 29.398 0.0 0.945 1.057
X1 0.042 0.032 1.302 0.193 -0.011 0.094
X0^2 0.011 0.025 0.447 0.655 -0.029 0.052
X0 X1 0.032 0.027 1.203 0.229 -0.012 0.076
X1^2 -0.024 0.019 -1.269 0.204 -0.056 0.007
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CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 0.99 0.046 21.503 0.0 0.914 1.066


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "===========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-----------------------------------------------------------\n", - "X0 1.001 0.034 29.398 0.0 0.945 1.057\n", - "X1 0.042 0.032 1.302 0.193 -0.011 0.094\n", - "X0^2 0.011 0.025 0.447 0.655 -0.029 0.052\n", - "X0 X1 0.032 0.027 1.203 0.229 -0.012 0.076\n", - "X1^2 -0.024 0.019 -1.269 0.204 -0.056 0.007\n", - " CATE Intercept Results \n", - "====================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------------------\n", - "cate_intercept 0.99 0.046 21.503 0.0 0.914 1.066\n", - "--------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 38 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 235 - }, - "id": "og2hubAj9cGV", - "outputId": "5f0c835f-dcff-4f9c-d1be-dc6741da2a10" - }, - "source": [ - "est.effect_inference(X[:5], T0=0, T1=1).summary_frame()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
01.2610.04726.7320.01.1841.339
11.7850.06826.2740.01.6731.897
22.4160.08329.0690.02.2802.553
31.4180.04829.8280.01.3401.497
4-0.2720.064-4.2660.0-0.378-0.167
\n", - "
" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "0 1.261 0.047 26.732 0.0 1.184 1.339\n", - "1 1.785 0.068 26.274 0.0 1.673 1.897\n", - "2 2.416 0.083 29.069 0.0 2.280 2.553\n", - "3 1.418 0.048 29.828 0.0 1.340 1.497\n", - "4 -0.272 0.064 -4.266 0.0 -0.378 -0.167" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 32 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aMqhTNEb4Zt4" - }, - "source": [ - "#### Debiased Lasso Inference for high-dimensional linear CATE models $\\theta(x)=\\langle\\theta, \\phi(x)\\rangle$" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "dtjN8aC99cGY" - }, - "source": [ - "from econml.dml import SparseLinearDML\n", - "\n", - "est = SparseLinearDML(featurizer=PolynomialFeatures(degree=3, include_bias=False))" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "W3xElnFJ9cGY", - "outputId": "89e6d659-d695-49c0-927c-66e3128c3e13" - }, - "source": [ - "est.fit(y, T, X=X, W=W)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 40 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 437 - }, - "id": "6XHdjW9I9cGY", - "outputId": "b8ad3ad6-7006-47ac-c431-d90c109cf0e3" - }, - "source": [ - "est.summary()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 0.99 0.065 15.182 0.0 0.882 1.097
X1 0.012 0.07 0.179 0.858 -0.102 0.127
X0^2 0.071 0.022 3.168 0.002 0.034 0.107
X0 X1 -0.012 0.034 -0.337 0.736 -0.068 0.045
X1^2 -0.019 0.027 -0.686 0.493 -0.063 0.026
X0^3 0.004 0.013 0.328 0.743 -0.017 0.026
X0^2 X1 0.026 0.025 1.025 0.305 -0.016 0.067
X0 X1^2 0.021 0.026 0.806 0.42 -0.022 0.065
X1^3 0.001 0.016 0.054 0.957 -0.026 0.027
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CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 0.945 0.051 18.628 0.0 0.862 1.029


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "=============================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "-------------------------------------------------------------\n", - "X0 0.99 0.065 15.182 0.0 0.882 1.097\n", - "X1 0.012 0.07 0.179 0.858 -0.102 0.127\n", - "X0^2 0.071 0.022 3.168 0.002 0.034 0.107\n", - "X0 X1 -0.012 0.034 -0.337 0.736 -0.068 0.045\n", - "X1^2 -0.019 0.027 -0.686 0.493 -0.063 0.026\n", - "X0^3 0.004 0.013 0.328 0.743 -0.017 0.026\n", - "X0^2 X1 0.026 0.025 1.025 0.305 -0.016 0.067\n", - "X0 X1^2 0.021 0.026 0.806 0.42 -0.022 0.065\n", - "X1^3 0.001 0.016 0.054 0.957 -0.026 0.027\n", - " CATE Intercept Results \n", - "====================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------------------\n", - "cate_intercept 0.945 0.051 18.628 0.0 0.862 1.029\n", - "--------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 41 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 235 - }, - "id": "nYXaiMBX9cGZ", - "outputId": "34e12072-ae19-40c5-d75f-72d96ff4682c" - }, - "source": [ - "est.effect_inference(X[:5], T0=0, T1=1).summary_frame()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
01.2440.06120.4580.0001.1441.344
11.7430.10217.1200.0001.5751.910
22.5280.09227.5530.0002.3772.679
31.3660.06122.5210.0001.2661.466
4-0.2360.085-2.7780.005-0.376-0.096
\n", - "
" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "0 1.244 0.061 20.458 0.000 1.144 1.344\n", - "1 1.743 0.102 17.120 0.000 1.575 1.910\n", - "2 2.528 0.092 27.553 0.000 2.377 2.679\n", - "3 1.366 0.061 22.521 0.000 1.266 1.466\n", - "4 -0.236 0.085 -2.778 0.005 -0.376 -0.096" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 42 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9kI_3o004cnQ" - }, - "source": [ - "#### Bootstrap-of-Little-Bags inference for forests CATE models $\\theta(x)$" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "JVKLLU5H9cGZ" - }, - "source": [ - "y, T, X, W = gen_data(2000, discrete=True)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "TzNqV3HW9cGZ", - "outputId": "e829ef4f-3016-431a-91d3-4864c9cca580" - }, - "source": [ - "from econml.dml import CausalForestDML\n", - "\n", - "est = CausalForestDML(discrete_treatment=True,\n", - " criterion='mse', n_estimators=1000)\n", - "est.tune(y, T, X=X, W=W)\n", - "est.fit(y, T, X=X, W=W, cache_values=True)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 44 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 451 - }, - "id": "PhogESjH9cGa", - "outputId": "97fea945-cc4e-4641-cb7b-e20f8e61064d" - }, - "source": [ - "est.summary()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Population summary of CATE predictions on Training Data\n" - ], - "name": "stdout" - }, - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Uncertainty of Mean Point Estimate
mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper
1.099 0.205 5.362 0.0 0.762 1.436
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Distribution of Point Estimate
std_point pct_point_lower pct_point_upper
1.086 -0.862 2.947
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Total Variance of Point Estimate
stderr_point ci_point_lower ci_point_upper
1.105 -0.862 2.965
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Doubly Robust ATE on Training Data Results
point_estimate stderr zstat pvalue ci_lower ci_upper
ATE 1.095 0.056 19.388 0.0 1.002 1.187
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Doubly Robust ATT(T=0) on Training Data Results
point_estimate stderr zstat pvalue ci_lower ci_upper
ATT 1.075 0.078 13.719 0.0 0.947 1.204
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Doubly Robust ATT(T=1) on Training Data Results
point_estimate stderr zstat pvalue ci_lower ci_upper
ATT 1.116 0.081 13.721 0.0 0.982 1.25


Note: The stderr_mean is a conservative upper bound." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Uncertainty of Mean Point Estimate \n", - "===============================================================\n", - "mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper\n", - "---------------------------------------------------------------\n", - " 1.099 0.205 5.362 0.0 0.762 1.436\n", - " Distribution of Point Estimate \n", - "=========================================\n", - "std_point pct_point_lower pct_point_upper\n", - "-----------------------------------------\n", - " 1.086 -0.862 2.947\n", - " Total Variance of Point Estimate \n", - "==========================================\n", - "stderr_point ci_point_lower ci_point_upper\n", - "------------------------------------------\n", - " 1.105 -0.862 2.965\n", - " Doubly Robust ATE on Training Data Results \n", - "=========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------\n", - "ATE 1.095 0.056 19.388 0.0 1.002 1.187\n", - " Doubly Robust ATT(T=0) on Training Data Results \n", - "=========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------\n", - "ATT 1.075 0.078 13.719 0.0 0.947 1.204\n", - " Doubly Robust ATT(T=1) on Training Data Results \n", - "=========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "---------------------------------------------------------\n", - "ATT 1.116 0.081 13.721 0.0 0.982 1.25\n", - "---------------------------------------------------------\n", - "\n", - "Note: The stderr_mean is a conservative upper bound.\n", - "\"\"\"" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 45 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 235 - }, - "id": "bZzv-0bo9cGa", - "outputId": "26e8e29b-691e-4fa4-d469-7b00ee463ac4" - }, - "source": [ - "est.effect_inference(X[:5], T0=0, T1=1).summary_frame()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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point_estimatestderrzstatpvalueci_lowerci_upper
X
01.9090.2308.2930.0001.5312.288
10.3080.1132.7350.0060.1230.493
2-0.1130.204-0.5530.580-0.4490.223
31.2770.1926.6650.0000.9621.593
42.3950.22510.6390.0002.0242.765
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" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "0 1.909 0.230 8.293 0.000 1.531 2.288\n", - "1 0.308 0.113 2.735 0.006 0.123 0.493\n", - "2 -0.113 0.204 -0.553 0.580 -0.449 0.223\n", - "3 1.277 0.192 6.665 0.000 0.962 1.593\n", - "4 2.395 0.225 10.639 0.000 2.024 2.765" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 46 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qqO9by3p9cGa" - }, - "source": [ - "# 4. Causal Scoring" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "9HwG--DU9cGa" - }, - "source": [ - "y, T, X, W = gen_data(2000, discrete=True)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Rr5El3LR9cGa" - }, - "source": [ - "#### Multitude of approaches for CATE estimation to select from" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "je0AkjG99cGb" - }, - "source": [ - "from econml.dml import DML, LinearDML, SparseLinearDML, NonParamDML\n", - "from econml.metalearners import XLearner, TLearner, SLearner, DomainAdaptationLearner\n", - "from econml.dr import DRLearner\n", - "\n", - "reg = lambda: RandomForestRegressor(min_samples_leaf=10)\n", - "clf = lambda: RandomForestClassifier(min_samples_leaf=10)\n", - "# A multitude of possible approaches for CATE estimation under conditional exogeneity\n", - "models = [('ldml', LinearDML(model_y=reg(), model_t=clf(), discrete_treatment=True,\n", - " linear_first_stages=False, cv=3)),\n", - " ('sldml', SparseLinearDML(model_y=reg(), model_t=clf(), discrete_treatment=True,\n", - " featurizer=PolynomialFeatures(degree=2, include_bias=False),\n", - " linear_first_stages=False, cv=3)),\n", - " ('xlearner', XLearner(models=reg(), cate_models=reg(), propensity_model=clf())),\n", - " ('dalearner', DomainAdaptationLearner(models=reg(), final_models=reg(),\n", - " propensity_model=clf())),\n", - " ('slearner', SLearner(overall_model=reg())),\n", - " ('tlearner', TLearner(models=reg())),\n", - " ('drlearner', DRLearner(model_propensity=clf(), model_regression=reg(),\n", - " model_final=reg(), cv=3)),\n", - " ('rlearner', NonParamDML(model_y=reg(), model_t=clf(), model_final=reg(),\n", - " discrete_treatment=True, cv=3)),\n", - " ('dml3dlasso', DML(model_y=reg(), model_t=clf(), model_final=LassoCV(),\n", - " discrete_treatment=True,\n", - " featurizer=PolynomialFeatures(degree=3),\n", - " linear_first_stages=False, cv=3))\n", - "]" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "it8Mb8219cGb" - }, - "source": [ - "#### Split the data in train and validation" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "dsqFr4Cz9cGb" - }, - "source": [ - "XW = np.hstack([X, W])\n", - "XW_train, XW_val, T_train, T_val, Y_train, Y_val = train_test_split(XW, T, y, test_size=.4)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9_X0_AQS9cGc" - }, - "source": [ - "#### Fit all CATE models on train data" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "fNWePOEi9cGc", - "outputId": "1e887546-43fe-4b1f-f238-06ec0d15ab2b" - }, - "source": [ - "from joblib import Parallel, delayed\n", - "\n", - "def fit_model(name, model):\n", - " return name, model.fit(Y_train, T_train, X=XW_train)\n", - "\n", - "models = Parallel(n_jobs=-1, verbose=1)(delayed(fit_model)(name, mdl) for name, mdl in models)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 9 out of 9 | elapsed: 17.7s finished\n" - ], - "name": "stderr" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5HrON5yV9cGc" - }, - "source": [ - "#### Train the scorer on the validation data" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "B4r0CXBP9cGc", - "outputId": "aae0216f-9ed1-4846-d0d4-eaff59d12e77" - }, - "source": [ - "from econml.score import RScorer\n", - "\n", - "# Causal score actually needs fitting on the test set!\n", - "scorer = RScorer(model_y=reg(), model_t=clf(),\n", - " discrete_treatment=True, cv=3,\n", - " mc_iters=3, mc_agg='median')\n", - "scorer.fit(Y_val, T_val, X=XW_val)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 51 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VaApR6459cGd" - }, - "source": [ - "#### Evaluate each of the trained CATE models on the validation data" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "WX92N2EP9cGd" - }, - "source": [ - "# Then we can evaluate every trained CATE model\n", - "rscore = [scorer.score(mdl) for _, mdl in models]" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "E2ZWa1Mu9cGd" - }, - "source": [ - "#### Calculate ideal score of each model, since we know ground truth" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "1e2SvCV89cGd" - }, - "source": [ - "expected_te_val = 1 + XW_val[:, 0]" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "yPF1_UOM9cGe" - }, - "source": [ - "rootpehe = [np.sqrt(np.mean((expected_te_val.flatten() - mdl.effect(XW_val).flatten())**2))\n", - " for _, mdl in models]" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "a4RafR039cGe" - }, - "source": [ - "#### Qualitatively different performance of each method" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 948 - }, - "id": "fkIgNP999cGe", - "outputId": "3aaa0a23-a756-4857-9daa-0f94c68b7280" - }, - "source": [ - "plt.figure(figsize=(16, 16))\n", - "rows = int(np.ceil(len(models) / 3))\n", - "for it, (name, mdl) in enumerate(models):\n", - " plt.subplot(rows, 3, it + 1)\n", - " plt.title('{}. RScore: {:.3f}, Root-PEHE: {:.3f}'.format(name, rscore[it], rootpehe[it]))\n", - " plt.scatter(XW_val[:, 0], mdl.effect(XW_val), label='{}'.format(name))\n", - " plt.plot(XW_val[:, 0], 1 + XW_val[:, 0], 'b--', label='True effect')\n", - " plt.ylabel('Treatment Effect')\n", - " plt.xlabel('x')\n", - " plt.legend()\n", - "plt.show()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hFddbtTU9cGf" - }, - "source": [ - "#### RScore correlates well with ideal score\n", - "\n", - "Higher `Rscore` implies smaller `PEHE`" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 279 - }, - "id": "i2w9dPuI9cGf", - "outputId": "3adc25ea-2f29-46e2-be2e-4a5162994be3" - }, - "source": [ - "plt.scatter(rootpehe, rscore)\n", - "plt.xlabel('rpehe')\n", - "plt.ylabel('rscore')\n", - "plt.show()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "r8eRryFl9cGf" - }, - "source": [ - "#### Choose CATE model with larger Rscore" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "wTaPVNH39cGf", - "outputId": "bb85de97-45cd-4a07-d659-38bc56183bfb" - }, - "source": [ - "mdl, score = scorer.best_model([mdl for _, mdl in models])\n", - "mdl" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 57 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "cULnL_mZ9cGg" - }, - "source": [ - "rootpehe_best = np.sqrt(np.mean((expected_te_val.flatten() - mdl.effect(XW_val).flatten())**2))" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 295 - }, - "id": "kLwPBbku9cGg", - "outputId": "125c9a16-1d42-4800-8ee5-c77c24d330f1" - }, - "source": [ - "plt.figure()\n", - "plt.title('RScore: {:.3f}, Root-PEHE: {:.3f}'.format(score, rootpehe_best))\n", - "plt.scatter(XW_val[:, 0], mdl.effect(XW_val), label='best')\n", - "plt.plot(XW_val[:, 0], 1 + XW_val[:, 0], 'b--', label='True effect')\n", - "plt.ylabel('Treatment Effect')\n", - "plt.xlabel('x')\n", - "plt.legend()\n", - "plt.show()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AWpMuY_49cGg" - }, - "source": [ - "# 4. Interpretation" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "PkjmyQgj9cGg" - }, - "source": [ - "y, T, X, W = gen_data(2000, discrete=True)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "S53DNzMd9cGg" - }, - "source": [ - "#### Fit any CATE model" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "XoHzf8Lo9cGg", - "outputId": "e5b36880-2294-4fbb-d4f7-b4e2535f1d9b" - }, - "source": [ - "from econml.dml import NonParamDML\n", - "\n", - "est = NonParamDML(model_y=RandomForestRegressor(min_samples_leaf=10), # Any ML model for E[Y|X,W]\n", - " model_t=RandomForestClassifier(min_samples_leaf=10), # Any ML model for E[T|X,W]\n", - " model_final=RandomForestRegressor(max_depth=2), # Any ML model for CATE\n", - " discrete_treatment=True, # categorical or continuous treatment\n", - " cv=5, # number of crossfit folds\n", - " mc_iters=1) # repetitions of cross-fitting for stability\n", - "\n", - "est.fit(y, T, X=X, W=W, cache_values=True) # fit the CATE model" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 61 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2N5UElbB9cGh" - }, - "source": [ - "#### Interpret its behavior with a single Tree" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "YnMz_1Hr9cGh", - "outputId": "cbca0155-0c49-424b-9366-c0496df4e3b5" - }, - "source": [ - "from econml.cate_interpreter import SingleTreeCateInterpreter\n", - "\n", - "intrp = SingleTreeCateInterpreter(max_depth=1)\n", - "intrp.interpret(est, X)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 62 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "JVQosW2K9cGh" - }, - "source": [ - "intrp.export_graphviz(out_file='cate_tree.dot')" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 248 - }, - "id": "Rvq9jg649cGh", - "outputId": "b43e7d52-09d1-45cd-b21f-08d2f51cfa55" - }, - "source": [ - "intrp.plot()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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MvGpWod+oAdi7OJCSlMyY74dj42BL35E/YG1rjbaODmO+H07GS0/QgRxJzbV1tDF8Jcm3tk524MlIV7/WzMJM7XtTczPiY+NzrXNifCLwIqvXq2IiY7CxL5Fvu/JSslzBKROlWoWbvOO/8zCrZi9n0B9DqVitkto5IxPjHMndAZITkzAyNXpWxoiUXMokPU1W9WTfR6J44cMkgu5HxKmUM6P+HYc8I4PLpy+zaNp8pv86hRlr/yXoyk3iYuKYvnomNvYlgOwE3gWNjb6uJ3FPcMZF9X1C/BPMrcxzLWv8bPXaj5N+wbmUS47zz3ui+bUrL+9qeOHK6Uv8M+ovvv6+E02+bJbjvKObI+H31ZO7xz6KIS01DYdnidEd3JzISM8g+mGU6mcP2WPEjq7ZY7jvI1G88GESQfcjpKOrS3XfGkQEh7Fq9nIA0tPSAdR2ZAjYdzTXNIZv48zhk2qB7MzhU5SuWDbXsg5ujljYWBL7KCbfzRufy61deXkXwwvBt+5n7wLR3Jdug3rkWqZqXW9mjvyLuJjHWFhnz+Q4sT8AA5kBHlUrAuBRpQIGhjKO7w+gbY/2AMTHxnP9wjUGjhvy4l7vIVG88OERQfcjEXzrPounL8S3ZQNKONjy5HE8u9bswLNmZQDKeZZHz0CPf3//B79uXxJ2P4wtSzciM5K903qcPHAcIxNjyniW4/D2A4TcDmZgHrsoSKVSev3Um39G/UVSYhKVfaqARMLDB+FcOXOZUbPGFtiuvJSuWOat2hEfG8/YfqMwMTel2Vct1ZKNm5qbqmYw1G3qy7r5q5k4eDyd+3fjcVQMK2cv54tv2qkCpa6eLm17tmfNnBXIDGXY2NuwZu5KbOxt8G3RQHXf95koXvhwiKD7kTCzNMfE3IS181YSFxOHsakx3r416DH0OyB7XuiwP0ewZMZCxv8wBtcybvz610gmDRn/TusxeOLPrF+whg3/W4u5tQU/Tf013wDYoFVDDAwNWL9wLbvX7kBbRxt7ZwfqtWxQqHa9L2H3Q3kcFQvAb91/UjvXyK8JP076BcjeImj8/EnM/eNfJg0eh4GhAW26+NGxn/ruvh36dEaRpWDV7OWkJCVToVolfpr6q9pDOU0liheKlkhiXgy9zyTmb+r54ogFu5fk+4BLeHdEEvPiSeReEARB0CARdAVBEDRIjOkK74RnDS92Xt9f1NUQhA+e6OkKgiBokOjpCnk6uvsIe9bt5H7QPeQZcmwd7ajdpC5fftOOVx/izZswm11rttN35ABadW4DZO8sfCiPJcDPderflS4/dKdVhaa5nvfyqcLERVPfTYME4QMggq6Qq/mT5rBr7Q6atW9Ju15fo2egT3DQPXau3k5aSqoqQQ1kJ885sT8AgIC9/qqg27FvF1p0aKUqN3vsTKxsrejY98V0qpdTIn7VuyM1P6ulVg/DXBLFCEJxJoKukMPpwyfZsWobP076RW2lmGcNL1p0aMWNS9fVygeeu8qTx/F41qxM4NkrxEbFYlXCCjtne7U0iDJDA0zNTdUS57zM1tEuz3OC8LEQY7pCDtuWb6F0hTK5Ls3V1dPNXjn2kmO7/TEyMWbA74MBOL73qEbqCdCqQlN2rdnO/Mlz6VCrLV3qfcX2lVsA2LthN72adKNDrbbMHvsP8gy52rX3bt5ldO/faO/dhg4+X/L3iGlquShio2L5e8Q0ejXpRrtqrenX+ju2r9zCy3Pbr569QqsKTbl+4Rrj+o+mnXdr+nzei5MHj2vmByAUOyLoCmoy5ZkEXblB1TrVCl3+1MHj1G5SB3sXB8p6lSfgDYOuUqEgKzNL7aswuSE2/G8tmfJMfvtrJL4tGrBg8jwWT1/IsT3+9Bs1gO6DenBw6wF2r92huiY8OIzfuv+ElrY2v0wbTv/Rgwg8d5W/R05TlUmMS8DcyoI+w/vz+7wJNP+qJStmLWP7ii056jBrzAwqVfdk1KyxOJV0Ytovk4mPiXujn4PwcRPDC4Kap08SkWfIsbazKVT5S6cu8jThqSqHQL3m9Vk4ZR5REY9y7JRQkNnjZjJ73Ey1Yx2+71TgzsFOJV34YcwgIPvB2/F9ARzcso/FB1aott0JPHeVU4dO4Ne9LQBr5q2khKMto2ePU+3aW8LRlp87DybkdjCuZdwoWb4UJctnp4hUKpVUqFqRlKQU9m/eq7rPc42/aErbnl8B4O5Rmi6+X3P++LlcM5MJnzYRdIW3cmyPP2aWZlR6llmsbjNf/jf1PwL2HKX9dx1e614d+nTG55Uk6BbP9mDLT5XaL3aSkEqllHC0xdjUWG2fMztne+7duKv6/uqZKzT/uiUoX2ztU7pCGfQN9Ll34w6uZdxQKBRsXrKRA5v3EhURRaY8e3gityTmL+9mYWxmgpmFmSp3gyC8TARdQY2xmQk6ujrEPIopsGxGegZnDp+kdpN6pCanAqCnr0eZSmUJ2Pv6QdfGvsQbZQczNH41wbp2ronYX07WnvgkgTVzV7Jm7soc93ve9i1LN7Jy1lI69OlMucrlMTQ24uTB42z837pc6vDq6+kgfyXBuyCACLrCK7R1tCnn5cGlkxfyzCH73PmAs6QkpXBwyz4ObtmX43xESLgqkfeHxtjUmHotGtCwdeMc5573rk8dOkmDVg1Ve74BnDlySmN1FD5OIugKOfh1/5IJA8dyZOchPmvVSO2cPCODGxev4+VThYA9R7GwtuDnP4e/UkbOHz+M4dgefzr168qHyLNGZcLuPci3Z52emqaW9D17PvIxTVRP+IiJoCvk4NOwNq27+PHPyOkEXbqBd/2a6Onr8eBOMDtXb8e7XnXKepbj7NHTNG/fMtctb6rUqUbA3qOvFXQfhUeqJQuH7PHTUuXd87jizXX+oRs/dhjIhEFjadimMUbGRsRERnPu2Fm6DeqBg6sjnjUrs2/DbtwrlMGyhBW71+7IMe1MEF6XCLpCrvqM+IFyXh7sXreDwzsOIc+QY+9sj2+LBnzxTTvO+J8mPTWd+p83zPX6Bp83ZNqwyYTcCca1tFuhXnPDwrVsWLhW7ZiNfQkWH1jx1u15laObE9NX/8OKmUuYOeovMuWZWNtZU7VOdcwss/d06/JDNxLinrDkr4Vo62jTyK8p1evXZO74We+8PsKnQyQxL4Y+xCTmguaJJObFk1gcIQiCoEEi6AqCIGiQCLqCIAgaJIKuIAiCBomgKwiCoEFiypigMXeu3Wb7qq0EXbpBZNjDQiWzAYh+GM28Cf9y9exlZIaGNG3XnM4/dFMlqgFYM3clV89e4fa1INJT09l2ZQ9a2lpq9+nVpBvRD6Ny3F9bR4etl3e9fQMFoRBE0BU05sal69y6chOPqhVIfJJQqGvkGXLG9BmOoZEhI/4eQ2xULP+b+h9SqZQuA7qryu3btBt7F0c8qlTk0skLud5r5KzfcyxumDRkPKUrvH6+B0F4UyLoChrTuosfft2+BLJ7nYVxfP8xHoVG8r/9y1Vb+6QkJbNq9nLaffs1+gb6ACw+sBKpVMrBLfvzDLqvrmx7cDeEuOjH1Pup/ps2SRBemxjTFTRGKn39t9vF4+cpV8VDbS+1us18SUtN48bFa29174A9R9HT18uxL5sgvE8i6AoftPDgcBzdnNSOWdvZoG+gT0RI+FvdO2DvUarVq46BoUHBhQXhHRFBV/igJSU+xeiVfLkAhiZGJCUkvfF979+8R0RIuGrHC0HQFBF0hU/Ssb3+6Bvo4+1bo6irInxiRNAVPmhGJsYkJyXnOJ6cmISRac4ecGGd2BdAjQY+qgdxgqApIugKHzRHN0fC74epHYt9FENaatob70px59ptIsMeUq+FmLUgaJ4IusIHrWpdb25euk5czGPVsRP7AzCQGeBRteIb3TNgrz8yIxne9aq/q2oKQqGJebqCxiTEPSHw3FUA0tPSCAsO4/i+7O1v6jbzBaCNZ3M69e2q2pesblNf1s1fzcTB4+ncvxuPo2JYOXs5X3zTTm1oIPDcVRLinnDn+m0ATh48jkQiwbmUC87uLqpySqWSgL3HqNmwNjq6uhpptyC8TARdQWMe3H3AlKETVN+fPHCckweOA7Dz+n4AFFkKFEqFqoyOrg7j509i7h//MmnwOAwMDWjTxY+O/bqo3XvVnOVcexbQAab+NBGATv270sX9xcq1oCs3iYmMxre5GFoQiobYOaIYEjtHCCB2jiiuxJiuIAiCBomgKwiCoEEi6AqCIGiQCLqCIAgaJGYvCG/t6O4j7Fm3k/tB95BnyLF1tKN2k7p8+U07Xn3YN2/CbHat2U7fkQNo1bkNAH+PmMahbQfyfY1O/bvS5YfutKrQNNfzXj5VmLho6hvVPyriEQe37qdNly8wNjPJt+zDBxF837Ink5ZMw7OG1xu9nvBpE0FXeCvzJ81h19odNGvfkna9vkbPQJ/goHvsXL2dtJRUev/WT1U2KyuLE/sDgOwFCs+Dbse+XWjRoZWq3OyxM7GytaJj3xfTwl5O7fhV74450jEaGhm+cRuiIqJYM3cln7VqVGDQFYS3JYKu8MZOHz7JjlXb+HHSLzTya6I67lnDixYdWnHj0nW18oHnrvLkcTyeNSsTePYKsVGxWJWwws7ZHjtne1U5maEBpuamlPMqn+vr2jra5XlOED50IugKb2zb8i2UrlBGLeA+p6unS2WfKmrHju32x8jEmAG/D6bP5704vvcoX3zTTiN13b12B9tWbCH6YRQyIxkly7kzZOLPRISEM6LnLwB83zJ7v7aK1T2ZsnQ6kD10smLWUuJj4vCoWpGvvuugkfoKHy8RdIU3kinPJOjKDdr2aF/o8qcOHqd2kzrYuzhQ1qs8AW8YdJUKBVmZWWrHJFJJnrtHXD1zmfmT59J1wDeUq+xBUuJTrp69QlpKKu4e7vQbNYB5E2bz24xRWNlaIzOUAXA78BZ//ToV35YN+KxVI24FBvH3iGmvXV9BeJkIusIbefokEXmGHGs7m0KVv3TqIk8TnqqShtdrXp+FU+YRFfGIEg62r/Xas8fNZPa4mWrH8ttZ+M7127iWceOr3h1Vx2o1qqP6t1Op7NwMJcuVwt7FQXV80+L1uJZx4+epvwFQrV51EuMT2bl622vVVxBeJqaMCRpxbI8/ZpZmVHr2xL9uM18kEgkBe46+9r069OnM3+tmq3217NQmz/Klyrtz/+Y9Fk79j+sXruXoJeflduAtajZUf2BXu3GdPEoLQuGInq7wRozNTNDR1SHmUUyBZTPSMzhz+CS1m9QjNTkVAD19PcpUKkvA3qO0f81xUhv7EpSuWPht0yvXqsqgP4aybfkWti3fjJGJMc2/akm3QT3Q0tbK87onj+MxszBTO2ZqaZZHaUEoHBF0hTeiraNNOS8PLp28QLdBPfItez7gLClJKRzcso+DW/blOB8REv7GCckLq8mXzWjyZTPiYh7jv+sIS2f8Dxt7G1p2bJ3nNWaW5jyJe6J2LOHxkzxKC0LhiOEF4Y35df+S24G3OLLzUI5z8owMrpy+BGRvdW5hbcGkJdPUvsbNn4S2tjbH9vhrrM4W1pa07dEe51KuhN4LBUBHJ7vvIZfL1cqWrliGM4dPqR07efCEZioqfLRET1d4Yz4Na9O6ix//jJxO0KUbeNeviZ6+Hg/uBLNz9Xa861WnrGc5zh49TfP2LXNdwVWlTjUC9h6lU7+uhX7dR+GRBF25qXZMR1eHUuXdcy2/as5ykhOTqehdCWMzE65fCCT0bgidnyVKt3dxQCqVsn/jHnxbfoahsSGObk606/UVP3cewvRfp/BZ68bcunqTM4dPvsZPSBByEkFXeCt9RvxAOS8Pdq/bweEdh5BnyLF3tse3RQO++KYdZ/xPk56aTv3PG+Z6fYPPGzJt2GRC7gTjWtqtUK+5YeFaNixcq3bMxr4Eiw+syLV86Qpl2LpsE/47D5GWmoatkx39Rw+kdpO6AJhamPHdr33YsmQjO1Ztw6NaRaYsnU5Zz/L8NOVXVsxawskDxylfpQJDJv7MyG9/fY2fkCCoE0nMiyGRxFwAkcS8uBJjuoIgCBokgq4gCIIGiaArCIKgQSLoCoIgaJAIuoIgCBokpowJGhN86z7/TZzNnWu3MbUw44tv2uHX7csCr7sQcI7V81YSeoCMr8UAACAASURBVCcEfZkBnjW96Dn0O6xsrQGQZ8iZ/usU7l6/TXxsPDIjQzyqVuCbH7/F4aUENq/WZfBX/TG3smDZ4dXvtJ2CkB/R0xU0IvFJIqN7/4aBoYwxc8bTuosfi6cv4EAuy4JfdufabcYPGIO9iwMjZo7hu1/7cDvwFuP6j0ahUACgUCjQ0tKiQ5/OjJ03gT4j+hMZ+pBR3/5KSlJyrvddMHkeJmam77ydglAQ0dMVNGLPup0olUp+mzEKfQN9KteqSkxkNGv/W0WTL5vled2J/QFYWFvy48SfVflyTcxMGN17OBHB4TiVckZPX49h00eoXVeyvDt9Wvbk+oVrVK9fU+3c8X3HiI6MovGXTTmyI+cSZkF4n0RPV9CIC8fP412vBvoG+qpjdZr5EhX+iIiQ8Dyvy8zMxEBmoJagXPZsPzSFUpHndSbPFo5kZmaqHU9PS2fRtAX0HPodOro6b9QWQXgbIugKGhEREoajm5PaMadn34cHh+V5XYPPP+Nh6EN2rNpKUmISj8IiWTFrCRW9K+Hi7qpWVvFsR4mYyGgWTJmHnZM9VWpXUyuzcdE6bOxtqNvM9900TBBekxheEDQiKTEJQxMjtWNGJsaqc3lxr1CG0bPHMWXoBOZPmgtk51IYv2BSjrKr/l3GugVrALB1smP8/ElqPevoh9FsWbKRKcumv3V7BOFNiZ6u8EELuR3MX79OoUGrhkxcNJXhf49GLpczYdDYHDtAtOzUhhlr/2XEP2MwszTn9z4jSXySqDq/ePoC6jSth3uFwidAF4R3TfR0BY0wMjEi5an6TIKkp0mqc3lZ+e9SXMuW5Icxg1TH3CuU5tum3Tlz5JQqUxiApY0lljaWlKlUlsq1q9KrcTf2rt/F19934ublG5z1P80/6+eoetbydDlKpZKkxCT09HXR0dV9l00WhFyJoCtohIOrU46x2/D72d+/Otb7soiQCKrVq652rISDLUYmxkSGPczzOpmhDDsnOx6FR2bfJzicjPQM+vv1zlG2Y6229P61L37d2xa6PYLwpkTQFTSiWl1vdqzaSnpaOnr6ekD2dDBbJ7t8t+qxtrPh3o27ascehUWSlPgUG/sSeV6X+CSR8JBwfBrVVr3+pCXq26cf2rqfc8fO8tuMUdg7279p0wThtYigK2hEiw6t2LFqK1OGTsCve1uCg+6xe+0Ofhg7WK1cG8/mdOrblU7PdnVo/nVLJg0ez6wxf1O3WT2SEpNYO28V1nY2eNerAcDRXUe4dPICVepUw9zKgpjIaLYu24Sung5N2zYHwNzaAnNrC7XXCjx3BR1dnVx3tBCE90UEXUEjTMxM+GPhFOZNmM24fqMwszCj50/f5VgYochSqM2/rd24Lr/8OZzNSzcQsMcffZkB5at40OPHbzEwNADAwc2RwzsOsnDKPJKfJmNhY4lnDS869x+fI9AKQlETO0cUQ2LnCAHEzhHFlZgyVkyJP5YCiPdAcSSCbjGkpa2d8jThaVFXQyhCSqWStJQ0HSCxwMLCB0UE3WJIKpUGXDp5QXRzPmG3A4OQamnFAo+Lui7C6xFBtxhKSUqet3ruytToh1FFXRWhCKSlpDJ/0tyUrMzM+UoxzlTsiAdpxZSunu4QqVQ62adRbWWFapUM9PTEaqqPnVyeScid4Iyju45kZmbIt6ampHZTKvNJtSZ8kETQLcYkEomjRCppbyCTVZVqSQ3f9D5ZmZl1JRLpA4VCUVFLS3pJIpFEKxQKZ4lEkiSRSmPfZZ0/BUql0lCpUNhLpdIQpRK9rKzM+lpaWueysrJqaGtr70QiebP7KhTpaalpt7IyszYB10Uvt3gSQfcTJ5FIJEAMcBM4CYQDw4DrwBClUhlUhNUrliQSiQUwA2gN/AfcA8YA+kANpVIZWoTVE4qYGNMVnAAjwBboDjQC2iqVyuYi4L4ZpVIZp1QqewDegCUwHYglO+hWy+dS4RMggq7QGtADrgHNlErlF0ql8lwR1+mjoFQqg5VKZV/Ak+xPEcZA16KtlVDUxPDCJ04ikRgDpZVK5cWirsvHTiKRuJD9OxdS1HURio4IuoIgCBqk0YQ3EonEWUtLq5OZmVlrpVJpKnnDp7hCsaFQKpXRcXFx64BNSqUyvqgr9DokEokt8LWOoV47CZgD4g1bfMkVCsWtzFT5KmCvUqnMLPCK90RjPV2JRNLAwMBgZ8eOHbXatGmjb2lpiQi6H7esrCwiIiJYs2ZN8pEjR5KTk5NrFpeP1hKJpIqWrpa/XWVnbftqLjI9Y32QivdrcaXMVJAYEa8M9r+VnBqfciYzTd5SqVRmFEVdNBJ0JRKJTE9PL3rPnj2Gn3322Xt/PeHDM2PGjKxx48bdTEhIqFTUdSmIRCKRaulqRVX7tp6lg7eriLQfEUWmgpMzD6Q+vh01LUue9XtR1EFTsxdaVqtWLUsE3E/XoEGDtBQKhbtEInEr6roUQh19U5meCLgfH6m2FI8vqhhItbV6FVkdNPEiurq6NVu2bGmiidcSPkza2trUrl07g+IxT7W6TUUHvaKuhPB+mJe0JkueaSuRSN54Fefb0FTQNTEyynvHV+HTYGpqKgWK5I3+mmQ6BjpiV5WPlEQiQaqtlQnIiuL1NbY4org+NHN1dWXUqFFFXY1CmTt3Ls2bN8fMzAyJRMLdu3dzlDl//jzdu3fH3d0diUSSa9sePXpEmzZtcHR0RE9PD0dHR3r16kVkZKRauR49eiCRSHJ8hYeH51q/YvUeKFaVfWHfsA3c2Fw8plzLUzK4suo0e3/ZwPZ+Kzn0+zYenMj5nlVkKgjacYV9v25kW5/l7Bu2gaAdV9TKbPl2aY6v49P35fnaRfmfK/6af0RWrFiBlpYWjRo1YvPmzbmWOXHiBKdPn6Zu3brExuaeyyYlJQUrKysmT56Mk5MToaGhjB07ltatW3PmzBm0tLRUZT09PZk/f77a9TY2Nu+uUcJH68KiAOKCY6nQtioySyMir4RxcfFxtPW0cfB2VZU7/79jxN2NpmzryhiVMCYlNom0hNQc9yvTshJ2lZ1U32sbfJiZ90TQ/YicOHECqVSKv79/nkF34MCBDB6cvQOvq6trrmVKlizJ4sWL1Y7Z2dnRtGlTgoKCqFChguq4sbExPj4+76YBwicjMz2TyCvhVOleC5e6pQGwLm9HfHAsEedDVEH30ZUwHl4MpdG4NhjbmeV7T0NrYyxKffh/8Is098KVK1do3LgxZmZmGBsb4+npydq1a1XnlyxZQq1atTAzM8Pa2prWrVtz/776HnwNGjSga9euLFiwABcXF4yNjenVqxdyuZwTJ05QrVo1jIyMaNy4MQ8fPlRd5+/vj0Qi4fDhwzRt2hSZTEapUqVYv359gfVev349lStXRl9fH0dHRyZOnKi2Z1lB7XpfpNKC/zsLUyY3lpaWAMjl8je6/mOQEBbH8en72DlgFTv6Z38cDj/z4v344Pgd/CfuYueAVewavIZTsw6SHKO+rVLAn3s4v/AYwUdvsfeXDezov5ILi4+jyFTw+E4UR8bvYHv/lRyfvo/U+BTVdTFBkWz5dikxNyM58dd+tvdbwf7fNhF+LrjAeoefC+bw2G1s67OcPT+v59bOK2rv14La9T4oFQpQKtF5pTeqY6CDUvGibg9O3MW6vG2BAbc4KdKerp+fHxUrVmTNmjXo6OgQGBjIkydPVOdDQ0P57rvvcHNzIykpiblz51KvXj3u3LmDTPZiDPzo0aNEREQwZ84cQkND+fHHH9HT0+P48eMMHz4cmUzGoEGD6N+/P1u3blWrQ48ePfjuu+8YOnQoK1asoHPnzri7u1O1atVc67xy5Uq++eYbBg0axJ9//sm1a9cYNWoURkZGqh5kQe3KTWZmwQtktLS0NDrUqFAoyMrKIiQkhN9++43atWvj5eWlVubSpUsYGxsjl8upWbMmU6ZMoVatWhqroyad/vcQxg7mePepj1RLSmJ4PPLUF/PrUx4n4+pbGkMrYzLT5dw/EsSxKXtoMqkt2novftVibz0iNT4Fr64+pD5OJnDdWbR0tHh8J4oyLT3R0tXi6pqzXFl5Cp+BjdTqcGHxcVzrlca9qQehp+5zfsExjGxMMHOxzLXOoafucWHRcUo1Lk+F9t4kRjzh5taLaOnp4N7Eo1Dtyo0iq+Dc6RKpJM/3q46BLvbVXLi9JxBje1MMLAyJuhpOzM1Iag1qrCoXHxyLXWUnLq88TdjJuygBOy8nvLr4oGukPsHk+qYLXF5xCh1Z9r0rtvdGR/bhDTEUWdCNiYnhwYMH7Nixg0qVsufLN27cWK3M77+/mLuclZVF48aNsba2Zs+ePbRr1051LjU1lR07dvB8hoS/vz///fcfp06dUn30ffjwIYMGDSIzMxNt7RfNbteuHWPGjAGgefPm3Lhxg6lTp7Ju3bocdVYqlQwfPpy+ffvy999/A9C0aVMyMjKYNGkSAwYMIC4ursB25UZHR6fAMkeOHKFBgwYFlntXunfvzqpVqwCoWrUqe/fuVfslqlq1Kj4+PpQvX56YmBimT59Ow4YNOXPmDJ6enhqrpyakP00j5XEyPoMaY+poDoCNh71amfJ+lVX/VioUWJe3Z/eQtUQFhquNUWbJs6g1qBHa+tn/57G3HhHsf4v6I1qqPh6nPUnl6pozKLIUSLVefDqxr+ZCuTbZr1OikiNPHz7h9p5AavRtkKPOSqWSG5su4NagLJ4da2RfU9EBRVYWt3dfpVSjcmQkZxTYrtxs+355gWXq/tIM63J2eZ737l2Ps/8d5dCYbUB2kK7yTW1sKrx4/fTEVEJP3MXU2YIa/RqQnpTOtfXnOL8ogNqDX/xeOddxx9bLCT0jPeJDHnNr5xUSI57g+1uLD+6ZaJEFXUtLS5ycnOjXrx+DBw+mYcOGqo+wzwUGBjJ8+HDOnDmj9tDnzp07auV8fHx4eUqau7s7hoaGamON7u7uZGVl8ejRIxwdHVXH/fz81O7l5+fHsmXLcq3z7du3CQ8Pp3379mo9088++4zhw4cTHh6Ok5NTge3KzblzBWdTLFu2bIFl3qU//viDQYMGcffuXSZOnEirVq0ICAhAVze79zBo0CC18i1btsTDw4M///yTlStXarSu75uuoR4GFoZcXnEK98YeWJW3Rc9IX61MQng81zddIP5+DBlJ6arjSdHqG/aal7RWBVwAQxtjtPS01cYjDW2MUSqUpCekYmDxYpbdyw+KAOyqOBF68l6udU6KSiQ1PgUHbxe1nql1OTtubLpIalwKBhaGBbYrNw1GtyqwjFEJ03zPX119lsSwOKp9Ww8DCxnRNyK5vOIUesb62Hplt1OpVKIEag5oqKqXtp42Z+Yc4WnkE9WwQ7VedVX3tSpri7GdKadmHiTmZmSh/ohoUpEFXalUyr59+xg+fDjdu3dHLpfTqFEj5syZg7u7O4mJiTRr1gxXV1dmz56Nk5MTurq6NGvWjLS0NLV7mZmpj/fo6urmegzIce2rT9qtra159OhRrnV+HvgbNmyY6/mwsDBcXFzybVdeKleunOe5516eNaAJbm5uuLm5UaNGDerWrYurqysbN26kc+fOuZaXyWQ0b96cY8eOabSemiCRSqj9YxNubLrA+UUBKLMUWJe3w6uLD0YlTJCnZnByxn5kVkZ4dfHBwMIQqbaUEzMOoJBnqd1L95WPvFJtaY6PwVLt7N5t1ivX6pkYqH9vrJ/rk3yAjKfZ7/Xj03KfOpUSn4zMyijfduXF1Mkiz3PPSfLJVfEk9DEhx25T79cWWJUpAWT/MUh7ksL1zRdVQVdHpoehtbHaHwKrcrYAJD5MyHOst0RFB7R0tXjy4LEIui8rX748W7duJT09nUOHDvHTTz/RpUsXzpw5w+nTp4mMjOTUqVO4uLgA2Q9xChobfV3R0dF4eHiovo+JicHW1jbXshYW2W+0ZcuWqV3z3POeaH7tysuHOLzwMmdnZywtLXM8yMzNh/Zx7l0xsTfDZ2AjsuRZxNyMJHD9Oc4vPEaDUa2IuxdDWkIq9Ud8jswq+1OXIlOBPOXd5lRJT0wF+xeBJv1pGvqmBrmW1THMHvOs9m1djO1zBqfnPdH82pWXtx1eSIrK7v2/GrzNnC2IOBei+t7YzhRFpvofnucK9Tb7AN+LH8SUMT09PVq2bMmtW7dU46spKdlPbp/3UCF71oBC8W43P922bZtaINu2bRvVq1fPtWzZsmWxt7cnLCyM7t27F3jv3NqVlw9xeOFlt2/fJjY2Fje3vFMnJCcns2fPHnx9fTVYM83T0tHC1tORpEcJ3Nx6CYCsjOzhpuc9VICI88HwjhNKRV4OUwtkkZfCMHe1yrWssa0p+mYyUuOSca6d96es53JrV17ednhB9mzIJCH0MVZlX3Ry4h88Rmb5YjjFtpIjQTuukP40DT3j7N5uzM3sT6Imz8agc/MoMJysjCzMnAvukWtakQXdq1ev8ssvv9CxY0fc3NyIiopizpw5qo/uPj4+yGQyevfuzZAhQwgKCmL69OmYmLzbFA6bNm3C3NycmjVrsnz5cq5evcrChQtzLSuVSpk2bRo9e/YkPj6exo0bI5FIuH37NocPH2bLli0Ftisv3t7eb92W8+fPExISwvXr1wHYu3cvtra2uLq6qu4fExPD0aNHgew/bEFBQWzcuBGA9u3bA/DXX3/x4MEDfH19sbKy4tatW0ydOpUyZcrQtm1bABISEmjVqhVdu3bF3d2dmJgYZsyYQUxMDMOGDXvrtnxoEsLiuLbhPI413JBZGZGemMb9I0FYlc8OgBalrNHS1ebispO4N/HgaWQCd/ddQ9ug4E8wr+PhhQfoynQxL2lN6Ml7JITHU6VH7VzLSqQSKn7lzcUlx8lIzsDGww4kEpKiEom5GYnPgIYFtisveQX6wjJztcLMxZLz/wugvF9lDCwMib7+kLBT9/DsVFNVzrVBGe4dvsnp2Ycp07ISGUnpXN94HseabhjZZMeC4KO3eBLyGGsPO/SM9IkPieXWrqtYli6BdQHtKApFFnRLlCiBlZUV48ePJzIyEgsLC1q2bMnUqVMBsLW1Zc2aNQwbNozWrVvj6enJ+vXrVb/078qSJUuYNGkSkydPxs7OjlWrVuUbADt37oyxsTGTJk1i7ty56OrqUrp0aTp27Fiodr1Ps2fPVnsIOHDgQAC++eYbli5dCsD169f56quvVGU2bdrEpk2bAFRzNz09Pdm5cyerV68mOTkZZ2dnvvjiC0aOHImBQfZHWT09PaysrJgwYQLR0dHo6+tTp04djh07ppq18THRMzFA10ifoB1XSHuSgq6hHiU8HanQPjt/j76pjOp9fLm24TynZh3C1NGc6n0bcGbukXdaj6o963B7VyC3dgeib2pA9d718g2ATj4l0dbX4dauqwT7ByHVkmJYwgTHGm6Fatf7ItWS4jOwETc2X+Dm1ktkJGdgaG2EV+eauDUopyqnK9Oj7s/NuLrqDOf+80eqo42DtwuVOrz4NGpobUzoyXtEnA8hM12OvqkMl7ql8fiiygc51KWRfLrGxsbzJ06c+P2rT7uLkr+/P5999hl37tzJ9wGX8O506NAhaf369QOUSmXu00M+EBKJZFSZzz3HV2hb9YP5jY0JiuT4tH00mdQ23wdcQuHs6L8yLTM901mpVMZo+rXFbsCCIAgaJIKuIAiCBn0QsxeKQoMGDRA7IQvFhXU5O75c1KOoqyG8A6KnKwiCoEEfXU937dq1zJs3j8uXL5Oenk7JkiVp164dQ4cOxdxcfV7fgAEDmDNnDrNnz+aHH34AshPg5LUM+Lnff/+dsWPH5vlktFGjRhw8ePDdNEj4aISfuc99/1skhMWhkGdhaG2MfTUX3JtWQNdQPXnLlVWnuX84CK8uNSnZsDyQnX82ryW/z5Vr40V5vyps+XZpruety9tR9+dm76Q9wpv5qILuoEGDmDt3Lr1792bYsGEYGhpy+fJlZs+eTVJSkipJDWQn0Hk+P3XdunWqoDt69Gj69u2rKtenTx8cHR0ZPXq06tjLuRuGDx9OmzZt1Ophapr/mnPh03Nl9RmCjwTh6luGMs0roqWnTUJYHPcPB5GZnqlKSAPZyXIizocAEH42RBV0y7b2wq3BiwUyl5afwsDckHKtXyQXMjB/sbDg1aTe8OEm9v6UfDRBd9u2bfz7778sW7ZMbbVYgwYN6Nu3LydOnFAr7+/vT1RUFA0bNuTIkSNERETg4OBAqVKlKFWqlKqcsbEx1tbWeSbqLlmypEjiLeTr4aVQ7h+6SbVv66qtDLMuZ4dbg7I8vhOtVj4m6BHpiWlYl7Mj5lYkqfHJGJgbZi8GsHkxXUxbXwc9Y708E3cXl6Ten5qPZkz3n3/+wdvbO9flufr6+jRqpJ6XdO3atZibm6u2milM8vJ3RSKRMHfuXAYPHoy5uTk2NjbMmjULgIULF+Lq6oq5uTl9+vQhI0N97f6lS5do2rQpRkZGmJmZ0aNHDxISElTnIyIi6NGjB66urshkMjw8PJg1a5baQ8PnCdyPHz9O69atMTQ0pGzZsmzZskUzP4BPzL0DNzBztcx1Ka6WjnaOhCzhZ4PRkelSuXt2XuKXcxG8b1u+Xcr9w0FcXX2GnQNXs2vIWu4dvAFA8NHb7Bu2gZ0DV3Np+ckcORGePHicnWC9/0p2DljFhUUBarknUuOTubAogH3DNrC93woOjtrCvYM31N6bz5O1x96J4tSsg2zvt5IDIzbz8OIDzfwANOCjCLpyuZxTp07RrFnhxqrkcjmbN2+mbdu2uLu74+Pjk2v+3MJQKBRkZmaqfRUmP8TkyZPJyMhg/fr1dOzYkcGDBzNs2DDWrl3LnDlzmDhxIkuXLmXu3Lmqa27duoWvry86OjqsWbOGefPm4e/vT48ePVRlnifsmTVrFrt37+b7779n1KhRzJw5M0cdvv32W+rXr8/WrVspX748nTp1yjPDmvBmFJkK4u5FU6KCQ6HLP7wYin01F4xKmGBR0rpQu0PkRqlUoshSqH29vCtDXm7vvooiS0GNvg1wrOHG1TVnubbhPBFng/Hq4oPHl1UJPXGX+0eCVNc8fZRAwNQ9SLSkVP++Pl7dahF76xEXFh9XlUl/moaeqQGenWtSa3BjXOuX4caWS6qg/rJLS05gVcYWnwENMbYz5dz8o6QlpOQoVxx9FMMLjx8/Jj09HWdn50KVP3DgAHFxcaqlux06dGDIkCGEhITkuW9YXvr06UOfPn3Ujo0cOZIJEybke52Hhwfz5s0Dsh+8bdiwgcWLF/PgwQMMDbPH5fz9/dmyZQtDhgwBYPz48bi5ubF9+3ZVmkc3Nzdq1apFYGAglSpVonLlyqo0kUqlkrp165KYmMiiRYtU93muZ8+e/PzzzwBUq1YNGxsb9uzZQ8+ePV/rZyDkLSM5DUWmAgPLwu08H33jIfLkdNUyXYfqbgSuPUty7FMMrYxf67UvLz/F5eWn1I6V/dwTj7a574rynLG9GZW7ZfeyrcvbEXE+hAfH79Dsz/Zo671IvP7wYijuTbL3ywvafgWZtTG1BjVE8mxLKEMrI45O2k1CeDymjuaYOVti5pydW1qpVGJZugSZqXIeBNxR3ec557rulG5eEQAzF0t2/7iWqMAI1X5qxdlHEXRf19q1a7GxseGzzz4D4KuvvuLHH39k3bp1/Prrr691r1GjRuVIhG5vX3D+ziZNmqj+LZVKcXNzw8LCQhVwITvx+sWLL7bTPnz4MH369EGpVKqSqHt7e2NoaMjFixepVKkSCoWC6dOns2jRIkJCQlTDE3p66k/HQX1HCwsLC2xsbIiIiChky4X3IfxsMHom+lg/yxnr4O1K4LqzRJwNoUzL18tpUbaVJ3ZV1Dsi+qayPEq/8PJwh0QqwdDKCB1DPVXAhewk609CH6u+jwmKxK1+GZRKUD5LmG7maoWWnjZPHjzG1NEcpULJnX3XeBBwh5THSSgys8u9nJkttzroGumhZ6yvtmdccfZRBF1LS0v09PQICwsrsGxaWhrbtm2jXbt2PH2avWmgTCajRo0abxR0XVxc3ihDWGETr7+cdD02NpZx48Yxbty4HPd73va//vqLUaNGMWrUKNWmnps3b2bKlCmFqsOrSd6Ft6NrqI9UW0pqXHKBZbPkmUReDsW+qgvytOwNQLX0tDB3syL8XPBrB12ZpdEbZQPLLaF6bonXX06wnpGURtD2KwRtv5Ljfs/bfmf/dW5suUS5Vp5YlLJBR6bLw4sPuL07MGcdDHK+3qvJ4IurjyLo6ujoUKtWLfbv388ff/yRb9k9e/aQmJjIkiVLWLJkSY7zt2/fpkyZMu+rqm/FwsKCjh070q1btxznnveut2zZQpcuXdTy927fvl1jdRTUSbWlWJSyIfr6Qzy+zP9jfVRgBJmpckJP3CX0xN0c558+SsDY9sOcjqhrqIdjdTecapfKce557zry4gOcfEqq9ngDiLwcqrE6fig+iqALMGTIEL744gtWrVpFly5d1M6lp6dz4sQJGjZsyNq1a7Gzs2P16tU5yrRu3Zp169apzcn9kDRs2JAbN27k27NOSUlRS/yelZXFhg0bNFE9IQ+lmnhwZvZhwk7fw8lHPShlybOIuxuNdXk7ws8Go29qgPf36kngFZkKTs06RMS5EMq1Vt+N+UNhXc6OxMgn+fasszIy1YYSXp6P/Cn5aIKun58fAwcOpEePHpw8eZLPP/8cmUxGYGAgs2fPpmXLltSsWZOdO3fy/fff57rtTdOmTV876N6/f5/Tp0+rHdPT06NKlSpv26Qcxo4dS/Xq1fnyyy/p3r07ZmZmhIaGsmvXLiZMmECZMmVo2LAhCxYswNvbGwcHB+bOnUt6enrBNxfeG/sqzpRsVJ4Li4/z+G4Mtp6OaOlqkRjxhPuHblLC0xHzklY8uhKOa/0yuW5xY1PBnvCzwa8VdJNjnhJ3T30OsFRbK8/t2t9Gg0T4YwAAHAxJREFUOb/K+P+xk9OzD+NcuxQ6Ml1SHicTdTWc8l9WwdjWFKtydoQcu42ZixUG5jLuHwlSjet+Sj6aoAswa9YsatWqxbx581ixYgXp6em4u7vTsWNHhg4dyo4dO0hJSclzY8UuXbrQuXNnrl27RsWKFQv1mpMnT2by5Mlqx1xcXAgJCXnb5uRQtmxZTp06xciRI+nZsycZGRk4OzvTvHlzSpTI3txv7NixREdH88svv6Crq0uPHj1o1aoV/fr1e+f1EQrPq3NNLEpZE3zkFmGn7qHIzMLQJjuZuHvTCjy6HEZWRiZONUvmer2TT0nOLzhGYnh8vtvUvOz27sAc46UyS0Oa/flVHle8OWNbU+qPaMmNzRe5uPg4iiwFBhaGlKjogP6zzTTL+1Um42ka1zecQ6KthUsdd+y8HLm84nQBd/+4fLJJzAXNE0nMhQ+FSGIuCILwiRBBVxAEQYNE0BUEQdAgEXQFQRA0SARdQRAEDfqopoy9D1evXmXAgAGcO3cOGxsbhg4dyuDBgwt9fWpqKuXKlSM0NFRtu/eHDx8yY8YM9u/fz/3797GysuLzzz9nwoQJajtcnD9/nlmzZnHy5Enu3btXqGQ6wqchPiSWewdvEncvmuTop4VKZpPf7hO+w1ti6Z6dfzclNomra84QfTMSqZYU+2ouVOpQXW15bpY8i1u7rhJ++j5pCSkYWBji6lsG96YV8txVRRBBN1+PHz+mSZMmeHt7s2PHDi5fvszPP/+MiYlJoTNxTZ06NdfFCRcvXmTbtm307t2b6tWrExYWxujRo7l27RpHjhxB+ixT04kTJzh9+jR169YlNjb2nbZPKN4e34km7n4Mlu42ZDwtXM6MV3efALi16ypx92Iwd8teTabIUnDinwNoaUvx7u1LZqqcaxvPk5aQSu3BL5IkXd94ngcBd/BoVw0TBzMe34nm+sYLAJRuVrh57p8iEXTz8d9//6FUKtmwYQMymYzGjRsTGhrKH3/8UaigGxoayowZM5g6dSr9+/dXO1e3bl1u3ryJtvaL/wI3Nzd8fX05f/48NWpkb98ycOBAVc/6ddNOCh+3Uo3K497EA4B9wwq31PvV3SeUCgXxwbHYV3VBqpX9hz7iXAjJ0Yk0ndQOmZURALqGupyadYi4+zH/b+/Oo5q88gaOfwOEsIVVEBANiwguVVBcEHXauo60ta+WY6eLo+NY9dWx1dbWarU6ntaO49HKuIzT9q22toOKtkytothC6wrYioKyVAHBDdn3hGzvHynByCLaMYrczzk5x9znPk+eaPLzyX3u/f1w9XcHDBnRAsb0IWC0oZyQe7AX1dcruZqaL4JuG8SYbhvi4+OZOHEidnZN6fCioqLIy8sjJyfnjvu/8cYbTJs2jd69ezfb5uzsbBJwAWMe3Ly8pqTVjVe8gnA7icVv/wlfkl2EqkpJt8G+xrbKwjIcPByNARfAvY8XSKAo/YqxTafVY3Vb9jGprRRzLLjqyMQ3ug3Z2dkEBwebtDU+z87ObnPfpKQkjhw50mIaxtacPGlION047isI99uVVNP8vWAYq709x63EwgKJREL19abSUIqInuR9n0lZbjEapZqijKsUJufi97jpd0YwJYYX2lBeXt4s52zjTa7y8vJW99Nqtbz66qssX74cN7f2JRdRqVS8+eabREREMGjQoHs/aUFoJ51Wx7WfLtNtsJ+x2gOAg4ecy0d/QV3XYMytW1lQhl6nR13bVPOsX1QY2gYNP7z3rbGt96QQfEd2/OoO95MIuvfBtm3bqK+vZ/78+e3eZ/78+eTn55OcnHwfz0wQmhRnXqehRoXPLUMLAD5D/bnw1RnOfHaCx6YORqNUk7bzpGE445YL4JyD6Vw9nU/Iy8OQezlTlltM1n/SkDnaNrtZJzQRQbcNLi4uJpV2ASoqKozbWlJTU8Py5cvZsGEDtbW1xjaA6upqamtrTUrygCFT2fbt2zlw4ABBQeLDKpjH1dR8bJztcAvsatIuk9sw6M8jObP9OPFv7AEJ+I7sBXqMGcNU1Uoy49IYOD2CHr8mLu8S5Im2Qcv52NP4jur1XxlzfhSJoNuGoKAgsrKyTNoan7cWHEtKSigrK+OPf/xjs20DBw5k0qRJfP3118a2nTt3smzZMj766COTummCcD8Zqg5fpsfwgBaDo3doDzwf86HmRiXWckONsm9fjTEWhqwtrkav1eHUw9VkP2eFK+p6NQ21KmRyG7O8l45GBN02TJgwgejoaOrr67G1NfwPHxsbi7+/f6slfTw9PUlMTDRpS0tLY+HChezcuZMBA5qSUB85coQ//elPLFu2jJkzZ96/NyIIt7l5/irquga6DfZrtY+FlYUxd2/ByUvotDrjLAe7X6sbNxadbFSRX4qlzAprh+aFUAUDEXTbMGfOHKKjo43Vgs+ePcuWLVvYtm2bST8rKytWrFjBihUrsLGxabEqBcDQoUONMxMyMzOZMmUKoaGhREZGmlSf8PHxwcfHB4Di4mJ++OEHwFCKJysri9jYWACee+65//ZbFjoQVbWSkuwbAGgatFTfqDSWv+kW5gvA17N2EPz0AJO6ZABXUvOxdbXHNcC9xWOn706lS6+uWFhZUppTRE58BgNeGGK8erVxssMzpDvpu1LRqtTGMd2c+Az8nwgWK9LaIIJuG9zc3EhISGDevHlERkbi4eHB2rVrmy2M0Gq16HR3V3YkOTmZqqoqUlJSCA8PN9n27rvvsnLlSgDOnz9PVFRTpv+9e/eyd+9eADEfspOrulpOytYk4/NrP13m2k+XAfifT6YDoNfpm31OtGot19MKDOOurQTHuuIafj5xCU19A3IvZ8JmjsDntqoWYTNHkhl3hl8OnUdZWY+dqz1Bkf0JnCAWRrRFVI4QzEZUjhAeFqJyhCAIQichgq4gCIIZiaArCIJgRiLoCoIgmFGnnL0QExPD1q1bSUtLQ6VS4e/vz5QpU1i0aFGzlWbz589n8+bNbNq0iXnz5gEwffp0duxo+15Q4wyE1u4Ojx49miNHjtzT+efn57N9+3YWLFiAq6trm30vXrxIYGAgiYmJrU5lEx4eV5JzyU3KprKwDJ1ai727HO9BCnqO64u1venc17NfnCL3+ywGvDgU/ycNmezaSlLeKPiZAfSeFMpXM7e3uN29txcj3hh/T+dfW1JNwfGLBIzuc8e5ujVFVSQs3ceIxeNxD/a6p9friDpd0F2wYAFbtmxh1qxZvPnmm9jb25OWlsamTZuoqalhw4YNxr5ardY4J3bXrl3GoLt8+XLmzJlj7Dd79mx8fHxYvny5sa1xni3A22+/zTPPPGNyHk5OTvf8HvLz81m1ahUvvfTSHYOu0HGc/TKZvMQsfEf1oteEfljKrKgsLCP3+yw0Kg39nx9i7KvX6Yxzcq+k5BuD7u1Jys98dhJbF3uCn+5vbLN1aVqG3mviY3iFdDc5Dytb03SNd6OupIas/5yl+7AAsUCiFZ0q6MbFxfGPf/yDHTt2MG3aNGP7448/zpw5czh+/LhJ/6SkJIqKinjyySdJTEzk6tWrdOvWjYCAAAICAoz95HI57u7uDBs2rMXX9ff3b3WbIABcO1NA7neZDJo5gh7Dm1J7ugd74fd4EKW/3DTpX5x1A1WVEvdgL4qzr1NfXouti32zJOVWNlJkchmuAR4tvq69u7zVbcL90anGdD/88EPCwsJMAm4jGxsbRo8ebdIWExODi4uLcQXa7t27zXKeAFu3biUoKAgbGxs8PDwYP348165dIykpiSeeeAKAwMBAJBKJybBBTEwMAQEB2NnZMX78eAoLC812zsK9u5RwAWdfN5OA28hSaoVHH2+TtispeUjtrAmZZlhYczU13xynCUBuYhYJS/cRN/szvn0thuPrD1NfXkdx1nWO/f0QAAlL9/HVzO0cXXuw6ZyTczm8ZC//mfv5r/vUmu2cHyadJuiq1WpOnjzJ+PHtG6tSq9Xs27ePyZMn07NnT4YNG8auXbvu6bV1Oh0ajcbk0dYKtsTERBYsWMCMGTM4dOgQ//rXvwgODqampoaBAweyefNmAPbs2cPJkyfZsmULAKmpqbz44ouEh4ezb98+hg8f3mLiHeHhotPoKLt0k659u7W7/7WfC/AepMChqyOu/u5cSc27844t0Ov16LQ6k4de1/qCqeKs65z7dzI9RvQkYtE4QqeFI/d0QqNS46xwY8CLhl90Q+Y+zu+WTmTAS4b/FMrzSkj96CiuAe4M/d8ncA3w4KdPjt3TOXd0nWZ4obS0FJVKRY8ePdrVPyEhgbKyMp5//nkApk6dymuvvUZ+fv5d1yqbPXs2s2fPNmlrq6rv6dOn6d+/P0uWLDG2Pfvss8Y/9+ljqIsVEhJiUmVi7dq19O/fn507dwKGhD0lJSVs2rTprs5XMK+GWiU6jQ5bN/s7dwZuXriGulaFzxBDsppug/1Ij0mhtqQa+y7yu3rttM9OkvbZSZO2tqoKl+eX4uTjStDEpjFi74EK45/l3oZ7FU7dXXHo2jTMkXMwHScfF8JmjQKg62M+NNQoyf3eNItfZ9BprnTvVkxMDB4eHsaf8lFRUUgkknu62n3nnXdITU01edxeqPJWoaGhnDlzhkWLFnHs2DE0Gk27XiclJYVJkyaZtE2ePPmuz1d4uF1JMS2x0y3MFyRwNSX/ro8V9FR/Hl/+lMnD74nWy+0493CloqCUczEplPxShE7bvpwj5XkleIWa3rDzHqRopfejrdMEXTc3N2QyWbvGOJVKJXFxcURGRlJdXU1FRQV2dnYMGTLknoKuQqEgLCzM5OHt7d1q/zFjxvDJJ5/w3XffMXLkSDw8PFiyZMkdg29RUREeHqY3RW5/Ljx8rO1tsLCyoL7szmOcWrWG62kFdH3MB7VSTUOdCkuZJS5+Xe5piMHOzQEX3y4mD1sXu1b7e/TxZuD0CIozr3P0g4MceC2GjNjTdwy+qqp6ZHJbk7bOmm+30wwvSKVSwsPDOXz4MKtXr26z78GDB6mqquLTTz/l008/bbY9Jyen1Xy6/y0zZsxgxowZXL9+nS+//JK33noLhULB3LlzW92na9eu3Lxpepf79ufCw8fCygLXAA9unr9Gn/9p+Wd9o6L0q2jq1RQcv0jB8YvNtlffqETuee/TEdtDMSIQxYhAlBV1FCbncj72J+zcHPBv4wpZ5miLqrrepE1Vrbyv5/mw6jRXugCvvfYaKSkpfPHFF822qVQqvv/+e8AwtODl5UViYqLJIz4+HqlUes831O6Fl5cXr7/+On379uXChQsAWFtbG8/5VoMHDyYuLs6kbd++feY5UeE3CRjbh/K8EgpPNV/YoFVrKc68DhiGFmycbBmxeLzJY/jCsUgsLcw6i8HG2Y7A8f2QeztTfc1QxsrCyhIAnUZr0tfFrwvXz5j+ymxMQ9nZdJorXYBJkybxl7/8henTp3PixAkiIyOxs7MjPT2dTZs2MXHiRIYOHcr+/ft55ZVXWlzBNW7cOHbt2mWyEOJOcnNzTZKUA8hkMkJDQ1vsv3LlSioqKhg1ahRubm4cPXqUjIwM3n33XcAwVczCwoKPP/6YP/zhDzg5OREUFMTixYsJDw/npZde4uWXX+bUqVPNgrDwcPIO7YH/6N789H/HKL1YjGd/HyytLam6WkHud5l07e+Di38Xbpy9gu/verW4gsujrzdXUvIIfnpAC6/Qstriasoumf4asrCyxFnRchXrzLgzqOsacOvlicxBRklOEVVXK4xJ0h26OoJEQv7RX/AZ4ofUzhq5pxOBE/rxw/vfcvqjH+keHkDZpWKup3XO6YydKugCREdHEx4eztatW/n8889RqVT07NmT559/nkWLFvHNN99QV1fHCy+80OL+L774Ii+88AIZGRn069e+ZM1r1qxhzZo1Jm0KhYL8/PwW+4eFhbF+/Xp27txJbW0tAQEBbN261XhTzN3dnQ0bNrBu3Tqio6MZOXIkSUlJDB061Fhzbe/evURERLB9+/Zm84+Fh9OAF4biGuBOXmI2hScvodNosfdwxGeIHz3H9eVGWiHaBg3db0sm3qj7MH9O/+tHqq6UG8vs3EnOgXRyDqSbtNm52TN+bVSL/Z19u3Dx8HkKT+WiUWmwd5cT8vIwuv16U0wmt6H/84P55dB5Lh3JpEsvD0a++Xtc/d0J+/MoLnz1M1d/uoxbTw8G/WkEx9Yduou/oUeDSGIumI1IYi48LEQSc0EQhE5CBF1BEAQzEkFXEATBjETQFQRBMCMRdAVBEMxIBN07OHfuHKNGjcLW1haFQsHGjRvvuE9SUhISiaTZ4/YENzqdjpUrV+Lt7Y29vT2///3vm00j0+l0rFq1Ch8fH2xtbQkPD28251fonCoLy/jxg4PEzfmc+MV7uJhw4a721zZoiF+8h69mbqemqMrYrtNoSd6SyKG3Yomb8zkHFsaQvCXRpA8YMpRdTDhPwrJ9/Gfu5xx6K5bz+35Gq25frpDOqtPN070bpaWljB07lrCwML755hvS0tJ44403cHR0ZMaMGXfcf8+ePSYVJLp3N0348d5777Fu3TrWr1+PQqFg1apVjBs3jvT0dGQyQ9b9999/nw8++ID333+f3r17s3nzZsaNG0daWhr+/i3P1xQefaoaJcfXH8ZZ0YXwBaOpLCgjY08qUlspihGB7TpGzsH0ZivHAPQ6PRaWFgQ91R97dzmqaiU5357j2LpDjP7rJKS/Vpa4dOQCGbtP0/vZUFwD3Km6Us75fT+jqW8wpngUmhNBtw3//Oc/0ev17NmzBzs7O8aMGUNBQQGrV69uV9C9PfXirerr6/n73//Ou+++yyuvvALAgAEDUCgU7Nq1i2nTpqFSqfjb3/7GO++8w8KFCwF48skn8ff3Z926dcY8ukLnk5+UjV5vyFtrJTMkOa8rqyXrm7PtCrp1pTVcPHyBvs8N4uxO019OltZWDJ79O5M25x5uJCzdR2lOEZ4DDBcPV1Ly8BniR1CkIc2je7AXyioll4/miKDbBjG80Ib4+HgmTpyInV1T1qWoqCjy8vLIycn5Tcc+ceIE1dXVREU1rfzx9PRkxIgRxMfHA4blwzU1NYwdO9bYx9rampEjR3LgwIHf9PpCx1aUcRXPx7phJWu6buoW5ktdSQ3VNyrvuH/G7tP0GB6A3Kt9yXEai2Lemk1Mr9U3q6cmtZW2mQRdEEG3TdnZ2QQHm2ZOanyenZ19x/0jIiKwtLQ0XpneuvovOzsbGxubZgnRg4ODjceurzdkZWpMcNNIJpNRUFBg3C50PtU3qnC4LWA2BtCaG1Ut7WJUnHWdmxeuETwppM1+ep2hqkRdWS3n/p2MvYccj1uqW/SI6MmV5FxuXriGRqmm7NJNcr/PajMfryCGF9pUXl6Os7OzSVtjifby8vJW93NycmLJkiWMGjUKqVTK/v37eeuttygvL+e9994z7t9SRWAXFxfjsQMCApBIJKSmphIS0vQFOX36NHq9nvLycmxtbZsdQ3j0qetUWNvddpX563N1naqlXQBDFeFz/04h+OkByBzazmeb+fUZsr89BxgKWA5fONbkyjpgdG809Q0cX38Yfr2eUIwIpM+zLSdyEgxE0L0PQkNDTTKIjRkzBmtra9avX8/bb7+Ng4NDu47j5OTE1KlTWbVqFX369CEoKIiNGzeSlWUocWJhIX6oCHcnLykHrVprLNneFr8ngvEK7UFdWS0XD2Vw4sMEfrcs0hisC05cIudgBn2fC8PVrwtV1yq48NUZZI429J0y6H6/lQ5LfGvb4OLiQmWl6fhYRUWFcdvdmDx5Mkql0pgTt6VjNx7/1mN/+OGH+Pr6MmLECNzd3dm7dy8LFixAKpXi5tZy+j3h0Se1k6GuU5u0qesajNtaolGqufD1GYKf6o+mwVB1QqPSGLdpVKbHs3Wxw8WvC90GKRi+cBwNNSryfzDcy9Dr9JyLSSFwQj96TehHlyBP/J8I5rGpg/klPgNVlRj6ao240m1DUFCQ8aqyUePzoKCgezqmRCIx7q9UKrl8+TIKRVOtqKysLJNjd+3alWPHjnH58mXq6uoICgri1VdfJSQkBKlUek/nIHR8ck/HZjfMGp87eDq2tAuqGiXqWlWLVXgT//oNXiHdGfaXltOASm2l2HvIqS2uBqDh12M5dXc16efcwxW9Tk9daS0yRzH01RIRdNswYcIEoqOjqa+vN46dxsbG4u/vf9flemJjY7GxsTFW8o2IiEAulxMbG8vrr78OGGqcHT16lI8++qjZ/o2BuaysjN27d7Ny5crf8M6Ejq5rv25c+i4TbYMGS2vD1/ja6Xzs3eWtlutprDhxq8rCMtJjUgmbNRInH9cW9wNDwK65UYV3qKGatrWDDZbWllRcLsUrpGn+ecXlUsCQk1domQi6bZgzZw7R0dFERUWxcOFCzp49y5YtW9i2bZtJPysrK1asWMGKFSsAmDt3Lu7u7gwePBhra2v279/Pli1bWLp0Kfb2hg+jjY0NixcvZuXKlTg6OhoXRygUCmPZd4AdO3ag0+nw8/MjNzeXNWvW0KtXL2bNmmW+vwjhoeP7eBCXvsskeWsSPcf2obKwnNzEbEKnhZv0+3rWDoKfHkDwMyFYSq1arDgB4OLnbiyZXpicy83z1+ja1xuZky31ZbVcPHweC6klipGGOcASCwm+I3vxS3y6ocabvztV1yrI/PoM3gMV4iq3DSLotsHNzY2EhATmzZtHZGQkHh4erF27ttnCCK1Wi07XNH8xODiYjz/+mPXr16NWqwkMDGTjxo3MmzfPZL9ly5ah0WhYsWIFlZWVjBo1ip07d5pMEdNqtXzwwQcUFBTg6urK1KlTWb16NVZW4p+uM5M52BCxaBxnvzjFyY1HkDna0i9qULOFEXqdnrstVCD3dKLw5CXOxaSgrmvAxtkO92Avhi0Iwcapac5636hBSO1lXD5+kez9Z5E52tIjoie97zAVrbMTlSMEsxGVI4SHRaeoHGGO4C483DrUZ6BDnaxwtx7kP65Zgq5SqSwvLy8XH+JOrqSkRAdUP+jzaIfqhprb5k8JjwydVodOrZUCNQ/i9c0SdDUazY9xcXEd4csm3CdKpZJTp07JgBMP+lza4diNc4VqcbH7aCrNKcLKRpqr1+sfyGRicw0vHMnOztZ8+eWX4lPcCen1epYuXdpgbW2dqtfrbzzo82mHn9X16pJLRzKb5z0UOjR1fQPpu1LrNEr1A0vRZ5YbaQASiaSfra3tj2FhYVZTpkyRu7m5iWWsjzitVkthYaHuiy++qC0oKLheU1MzXK/Xlz7o82oPiUTia2ltdVLu7WznM8RXLnO0lTQubBE6Hp1GS2VhWUPhyVyNVqPdpVVp/qzX63V33vO/z2xBF0AikTgAkXK5fLxUKnVDLEN+1GmVSuXVurq6OCBRr9d3qHFSiURiA4y3lFk9ZWFl4SFBIj6vHZRer1dqVJosvVa3G8jQP8CxI7MGXUEQhM5O/M8tCIJgRiLoCoIgmJEIuoIgCGYkgq4gCIIZiaArCIJgRiLoCoIgmJEIuoIgCGYkgq4gCIIZiaArCIJgRiLoCoIgmJEIuoIgCGYkgq4gCIIZiaArCIJgRv8PTM7cwIYvyIIAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WCtJ48zI9cGi" - }, - "source": [ - "#### Make tree-based policy recommendations from CATE model" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SZL7vB1_9cGi", - "outputId": "b085ab61-7631-4959-b5e8-ed458e8e80d1" - }, - "source": [ - "from econml.cate_interpreter import SingleTreePolicyInterpreter\n", - "\n", - "intrp = SingleTreePolicyInterpreter(max_depth=1)\n", - "intrp.interpret(est, X, sample_treatment_costs=0.2)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 65 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "-tWTr9ha9cGi" - }, - "source": [ - "intrp.export_graphviz(out_file='policy_tree.dot')" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 279 - }, - "id": "J90REc4o9cGi", - "outputId": "44988977-d39f-4fec-cca2-fe02014c6bc9" - }, - "source": [ - "intrp.plot()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YMzp0si39cGj" - }, - "source": [ - "#### Interpret CATE model with SHAP" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "FS1Uy2V-9cGj" - }, - "source": [ - "shap_values = est.shap_values(X)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 194 - }, - "id": "cXdMGuBY9cGj", - "outputId": "f980abf2-c1ed-4305-f76c-48fae99331d8" - }, - "source": [ - "import shap\n", - "\n", - "# effect heterogeneity feature importances with summary plot\n", - "shap.summary_plot(shap_values['Y0']['T0_1'])" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 193 - }, - "id": "rowyWQ_l9cGj", - "outputId": "0dc74436-6ca5-417b-e45c-b260c20b6e19" - }, - "source": [ - "shap.initjs()\n", - "# explain the heterogeneity of the effect of any single sample\n", - "shap.force_plot(shap_values['Y0']['T0_1'][0])" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "
\n", - "
\n", - " Visualization omitted, Javascript library not loaded!
\n", - " Have you run `initjs()` in this notebook? If this notebook was from another\n", - " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", - " this notebook on github the Javascript has been stripped for security. If you are using\n", - " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", - "
\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 70 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dA1nRbFK9cGk" - }, - "source": [ - "# 5. Validation and Sensitivity" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Nds7Fwyg9cGl" - }, - "source": [ - "y, T, Z, X, W = gen_data_iv(2000)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-zHcWkF19cGl" - }, - "source": [ - "#### Instantiate any CATE model" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "0nMA97Jh9cGl" - }, - "source": [ - "from econml.iv.dml import OrthoIV\n", - "\n", - "est = OrthoIV(model_y_xw=RandomForestRegressor(), # ML model for E[Y|X,W]\n", - " model_t_xw=RandomForestRegressor(), # ML model for E[Y|X,W]\n", - " model_z_xw=RandomForestRegressor(), # ML model for E[Y|X,W]\n", - " discrete_treatment=False, # categorical/continuous treatment\n", - " discrete_instrument=False, # categorical/continuous instrument\n", - " cv=2, # number of crossfit folds\n", - " mc_iters=1) # repetitions of cross-fitting for stability" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Z91lWEiQ9cGl" - }, - "source": [ - "#### Enable dowhy capabilities" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "QEe9XMai9cGm" - }, - "source": [ - "import dowhy\n", - "est = est.dowhy" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BIWgh7Ce9cGm" - }, - "source": [ - "#### Then fit" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "roPf-t439cGm", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "4d24cac9-58fa-44fa-87e9-f8e57ec68176" - }, - "source": [ - "est.fit(y, T, Z=Z, X=X, W=W, cache_values=True)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 74 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5tlyZMcP9cGn" - }, - "source": [ - "#### Use it as a normal EconML cate estimator" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "lHr9Ooaa9cGn", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 290 - }, - "outputId": "e86a4da8-cbac-4271-ae99-8d312fd1949c" - }, - "source": [ - "est.summary()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Coefficient Results
point_estimate stderr zstat pvalue ci_lower ci_upper
X0 0.99 0.035 27.933 0.0 0.932 1.049
X1 -0.012 0.027 -0.441 0.659 -0.057 0.033
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CATE Intercept Results
point_estimate stderr zstat pvalue ci_lower ci_upper
cate_intercept 1.018 0.026 38.577 0.0 0.975 1.061


A linear parametric conditional average treatment effect (CATE) model was fitted:
$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$
where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:
$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$
where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " Coefficient Results \n", - "========================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------\n", - "X0 0.99 0.035 27.933 0.0 0.932 1.049\n", - "X1 -0.012 0.027 -0.441 0.659 -0.057 0.033\n", - " CATE Intercept Results \n", - "====================================================================\n", - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "--------------------------------------------------------------------\n", - "cate_intercept 1.018 0.026 38.577 0.0 0.975 1.061\n", - "--------------------------------------------------------------------\n", - "\n", - "A linear parametric conditional average treatment effect (CATE) model was fitted:\n", - "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n", - "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n", - "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n", - "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.\n", - "\"\"\"" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 75 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "o1z0z-E79cGo", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 235 - }, - "outputId": "5cf7bc63-16c6-4eb4-9cd7-3ecd7a7128fd" - }, - "source": [ - "est.effect_inference(X[:5]).summary_frame()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " point_estimate stderr zstat pvalue ci_lower ci_upper\n", - "X \n", - "0 0.286 0.045 6.362 0.0 0.212 0.360\n", - "1 2.730 0.071 38.618 0.0 2.613 2.846\n", - "2 0.678 0.030 22.866 0.0 0.630 0.727\n", - "3 0.760 0.038 19.951 0.0 0.697 0.823\n", - "4 2.539 0.086 29.605 0.0 2.398 2.680" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 76 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cAPR5eMZ9cGo" - }, - "source": [ - "#### But now we also have DoWhy capabilities: Sensitivity Analysis" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "9aUCWN489cGo" - }, - "source": [ - "ref_res = est.refute_estimate(method_name=\"add_unobserved_common_cause\",\n", - " effect_strenght_on_treatment=0.05,\n", - " effect_strength_on_outcome=0.5)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "7bTLzM_H9cGp", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "7b5fdb67-e26d-46f0-b46d-572001977533" - }, - "source": [ - "print(ref_res)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Refute: Add an Unobserved Common Cause\n", - "Estimated effect:1.0314669223854354\n", - "New effect:0.9983858330683743\n", - "\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MuaLIl6X9cGp" - }, - "source": [ - "# 6. Policy Learning" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Z5NSUCzK9cGp" - }, - "source": [ - "y, T, X, W = gen_data(2000, discrete=True)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_oGYT0HP9cGp" - }, - "source": [ - "#### Fit a Doubly Robust policy tree" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "PLk_-21M9cGp" - }, - "source": [ - "from econml.policy import DRPolicyTree\n", - "\n", - "est = DRPolicyTree(max_depth=2, min_impurity_decrease=0.01, honest=True)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "DVBwu-oh9cGq", - "outputId": "8972c75a-1793-4611-959c-708e85ea5796" - }, - "source": [ - "est.fit(y, T, X=X, W=W)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 81 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WNpPMq0Z9cGq" - }, - "source": [ - "#### Visualize treatment policy" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 279 - }, - "id": "4YnPJkJW9cGq", - "outputId": "5b9b02ea-01dd-4c3a-d598-4fb61b8e02ce" - }, - "source": [ - "est.plot()" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JjBym5Iw9cGr", - "outputId": "2fdf00f7-9f6f-494e-be9a-84b85ad7d30c" - }, - "source": [ - "est.feature_importances_" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([1., 0.])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 83 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "L7q7UxZG9cGr" - }, - "source": [ - "#### Produce recommended treatments" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "sZ_E-Tjk9cGr", - "outputId": "4773b57f-bc7e-4f70-f65c-50ad9471041e" - }, - "source": [ - "est.predict(X[:100])" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n", - " 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n", - " 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 84 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Zb3MFbCq9cGs" - }, - "source": [ - "#### Fit a Doubly Robust policy forest" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "N4CAMtPQ9cGs" - }, - "source": [ - "from econml.policy import DRPolicyForest\n", - "\n", - "est = DRPolicyForest(n_estimators=100, max_depth=2,\n", - " min_impurity_decrease=0.01, honest=True)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "jK5vBCCm9cGs", - "outputId": "1b97cc54-6324-42c0-e403-fba3dc3dee14" - }, - "source": [ - "est.fit(y, T, X=X, W=W)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 86 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lhiGgCKD9cGt" - }, - "source": [ - "#### Produce recommended treatments" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "lAGuJ1wB9cGt", - "outputId": "b1590aad-d1d1-4657-8e65-012ae7abec58" - }, - "source": [ - "est.predict(X[:100])" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n", - " 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 87 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3xWXtC8k9cGt" - }, - "source": [ - "#### Plot one of the trees" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 279 - }, - "id": "Xen6bm7Y9cGt", - "outputId": "25f978fc-a5d0-4395-9bb2-a0b089ff2828" - }, - "source": [ - "est.plot(0)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "tags": [], - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "c90e1Sei80dv" - }, - "source": [ - "" - ], - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file From 83d64851312c882ba3f5f3d24591a25bee5f8973 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Wed, 12 Apr 2023 09:59:09 -0700 Subject: [PATCH 10/33] Update issue templates --- .github/ISSUE_TEMPLATE/task-template.md | 10 ++++++++++ .github/ISSUE_TEMPLATE/upgrade.md | 4 ++-- 2 files changed, 12 insertions(+), 2 deletions(-) create mode 100644 .github/ISSUE_TEMPLATE/task-template.md diff --git a/.github/ISSUE_TEMPLATE/task-template.md b/.github/ISSUE_TEMPLATE/task-template.md new file mode 100644 index 0000000..805c6f6 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/task-template.md @@ -0,0 +1,10 @@ +--- +name: Task template +about: Tasks related to launch and organize the KDD 2023 workshop +title: "[Task] " +labels: '' +assignees: '' + +--- + +* Deadline: diff --git a/.github/ISSUE_TEMPLATE/upgrade.md b/.github/ISSUE_TEMPLATE/upgrade.md index f854fcb..b60dfa8 100644 --- a/.github/ISSUE_TEMPLATE/upgrade.md +++ b/.github/ISSUE_TEMPLATE/upgrade.md @@ -1,8 +1,8 @@ --- name: "[fastpages] Automated Upgrade" -about: "Trigger a PR for upgrading fastpages" +about: Trigger a PR for upgrading fastpages title: "[fastpages] Automated Upgrade" -labels: fastpages-automation +labels: '' assignees: '' --- From 5830ca87ebb0e2283b60ad51839e6f47ae812aa0 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Fri, 14 Apr 2023 15:50:08 -0700 Subject: [PATCH 11/33] add the CfP page --- _pages/cfp.html | 61 +++++++++++++++++++++++++++++++++++++++++++++++++ index.html | 10 ++------ 2 files changed, 63 insertions(+), 8 deletions(-) create mode 100644 _pages/cfp.html diff --git a/_pages/cfp.html b/_pages/cfp.html new file mode 100644 index 0000000..54f27b0 --- /dev/null +++ b/_pages/cfp.html @@ -0,0 +1,61 @@ +--- +permalink: /cfp.html +layout: default +title: Call for Paper +search_exclude: true +--- + +# **Call for Paper** + +## **Important Dates** + +All deadlines are at 11:59 PM [AoE](https://www.timeanddate.com/time/zones/aoe). +* April 30th, 2023: CMT submission portal opens +* June 9th, 2023: Workshop paper submission deadline +* July 10th, 2023: Paper decision notifications +* July 24th, 2023: Camera-ready deadline +* August 7th, 2023: Workshop + +## **Submission Link** + +CMT submission portal will open on April 30th. + +## **Aim and Scope** + +The workshop aims to bring together researchers and practitioners from academia and industry to share their experiences +and insights on applying causal inference and machine learning techniques to real-world problems in the areas of +product, brand, policy, and beyond. + +We welcome papers on a variety of topics, including but not limited to the following: +* Industry use cases where causal inference and machine learning are used in practice +* Challenges and opportunities for using causal inference and machine learning in industry settings +* Techniques for incorporating causal inference into machine learning models +* Methodologies for evaluating causal machine-learning models in practice + +We encourage submissions from researchers and practitioners working in industry, government, and academia. We welcome +papers that present new research results, works in progress, or case studies that showcase the application of causal +inference and machine learning techniques to real-world problems. + +All submissions will be peer-reviewed by the program committee, and accepted papers will be presented as contributed +talks or posters during the workshop. + +## **Submission and Formatting Instructions** + +* Submissions are single-blind—author names and affiliations should be listed. +* Submissions are limited to 6 pages (excluding references), must be in PDF, and use the ACM Conference Proceeding +template (two-column format). +* The recommended setting for Latex documents is: +`\documentclass[sigconf, review]{acmart}`. +* Additional supplemental material focused on reproducibility can be provided. Proofs, pseudo-code, and code may also be +included in the supplement, which has no explicit page limit. +* The supplementary material should be included in the same pdf file as the main manuscript. The main body of the paper +should be self-contained since reviewers are not required to read the supplementary material. The supplementary material +will not be included in the proceedings. +* Submissions violating these formatting requirements will be desk-rejected. +* The Word template guideline can be found [here](https://www.acm.org/publications/proceedings-template). +* The Latex/overleaf template guideline can be found +[here](https://www.overleaf.com/latex/templates/association-for-computing-machinery-acm-sig-proceedings-template/bmvfhcdnxfty). + +For any questions or inquiries, please contact the workshop organizers at [jeong@uber.com](mailto:jeong@uber.com) and +[zyzheng@berkeley.edu](mailto:zyzheng@berkeley.edu). We +look forward to your submissions! \ No newline at end of file diff --git a/index.html b/index.html index 2610784..e8457e2 100644 --- a/index.html +++ b/index.html @@ -31,12 +31,6 @@ The tutorial assumes some basic knowledge in statistical methods, machine learning algorithms and the Python programming language. -## **Important Dates** - -* May 23, 2023 [AoE]: Workshop paper submission deadline -* June 23, 2023: Paper decision notifications -* August 7, 2023: Workshop - ## **Outline** | **Title** | **Duration** | Link | @@ -99,7 +93,7 @@ * Xinwei Ma, UC San Diego * [Zeyu Zheng](mailto:zyzheng@berkeley.edu), UC Berkeley, Amazon - main contact -### CausalML Team +### [CausalML](https://github.com/uber/causalml) Team * Jing Pan, Snap, CausalML * Yifeng Wu, Uber, CausalML @@ -109,7 +103,7 @@ * [Jeong-Yoon Lee](mailto:jeong@uber.com), Uber, CausalML - main contact * Zhenyu Zhao, Tencent, CausalML -### EconML Team +### [EconML](https://github.com/py-why/EconML) Team * Fabio Vera, Microsoft Research, EconML * Eleanor Dillon, Microsoft Research, EconML From 45bf3ccb943841d0e750938ab9350081eccbe9b5 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Fri, 14 Apr 2023 17:52:31 -0700 Subject: [PATCH 12/33] replace cfp.html with cfp.md --- _pages/{cfp.html => cfp.md} | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) rename _pages/{cfp.html => cfp.md} (98%) diff --git a/_pages/cfp.html b/_pages/cfp.md similarity index 98% rename from _pages/cfp.html rename to _pages/cfp.md index 54f27b0..4d08a7c 100644 --- a/_pages/cfp.html +++ b/_pages/cfp.md @@ -1,5 +1,5 @@ --- -permalink: /cfp.html +permalink: /cfp/ layout: default title: Call for Paper search_exclude: true @@ -58,4 +58,4 @@ For any questions or inquiries, please contact the workshop organizers at [jeong@uber.com](mailto:jeong@uber.com) and [zyzheng@berkeley.edu](mailto:zyzheng@berkeley.edu). We -look forward to your submissions! \ No newline at end of file +look forward to your submissions! From b44ce722aaef571639f3b611cd3b5ba82e8d2416 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Fri, 14 Apr 2023 20:10:19 -0700 Subject: [PATCH 13/33] update abstract, add the link to CfP --- README.md | 37 +++++++++++++++++++++++++------------ index.html | 36 ++++++++++++++++++++---------------- 2 files changed, 45 insertions(+), 28 deletions(-) diff --git a/README.md b/README.md index 77b6824..7b97efc 100755 --- a/README.md +++ b/README.md @@ -8,23 +8,32 @@ ## **Schedule** -* Long Beach Convention & Entertainment Center, 300 E Ocean Blvd, Long Beach, CA 90802 ([Google Map](https://goo.gl/maps/1N3XGEovGgJqXAV98)) +* Long Beach Convention & Entertainment Center, 300 E Ocean Blvd, Long Beach, CA 90802 ([Google +Map](https://goo.gl/maps/1N3XGEovGgJqXAV98)) * 9:00 AM - 1:00 PM August 7, 2023 [PDT] ## **Abstract** -In recent years, both academic research and industry applications see an increased effort in using machine learning methods to measure granular causal effects and design optimal policies based on these causal estimates. Open source packages such as [CausalML](https://github.com/uber/causalml) and [EconML](https://github.com/microsoft/econml) provide a unified interface for applied researchers and industry practitioners with a variety of machine learning methods for causal inference. The tutorial will cover the topics including conditional treatment effect estimators by meta-learners and tree-based algorithms, model validations and sensitivity analysis, optimization algorithms including policy leaner and cost optimization. In addition, the tutorial will demonstrate the production of these algorithms in industry use cases. +The increasing demand for data-driven decision-making has led to the rapid growth of machine learning applications in +various industries. However, the ability to draw causal inferences from observational data remains a crucial challenge. +In recent years, causal inference has emerged as a powerful tool for understanding the effects of interventions in +complex systems. Combining causal inference with machine learning has the potential to provide a deeper understanding of +the underlying mechanisms and to develop more effective solutions to real-world problems. -## **Target Audience for the Workshop** +This workshop aims to bring together researchers and practitioners from academia and industry to share their experiences +and insights on applying causal inference and machine learning techniques to real-world problems in the areas of +product, brand, policy, and beyond. The workshop welcomes original research that covers machine learning theory, deep +learning, causal inference, and online learning. Additionally, the workshop encourages topics that address scalable +system design, algorithm bias, and interpretablility. -Anyone who is interested in causal inference and machine learning, especially economists/statisticians/data scientists who want to learn how to combine causal inference and machine learning with real industry use cases incorporated in large scaled machine learning systems at companies such as Microsoft, TripAdvisor and Uber. -The tutorial assumes some basic knowledge in statistical methods, machine learning algorithms and the Python programming language. +Through keynote talks, panel discussions, and contributed talks and posters, the workshop will provide a forum for +discussing the latest advances and challenges in applying causal inference and machine learning to real-world problems. +The workshop will also offer opportunities for networking and collaboration among researchers and practitioners working +in industry, government, and academia. -## **Important Dates** +## **Call for Paper** -* May 23, 2023 [AoE]: Workshop paper submission deadline -* June 23, 2023: Paper decision notifications -* August 7, 2023: Workshop +Please check the [Call for Paper](/cfp/) page for details on important dates and submission guidelines. ## **Outline** @@ -49,7 +58,11 @@ The tutorial assumes some basic knowledge in statistical methods, machine learni ### Raif Rustamov, Amazon -Video creatives have a substantial impact on consumer experiences and brand perceptions, but evaluating their effect on shopper behavior remains a complex challenge. In this talk, we introduce the Creative Optimality Gap (COG), a novel metric developed using causal-inferential machine learning methodologies to quantify the relative optimality of video creatives. We provide an example application of this approach in assessing the effectiveness of video creatives for brand advertising at Amazon. +Video creatives have a substantial impact on consumer experiences and brand perceptions, but evaluating their effect on +shopper behavior remains a complex challenge. In this talk, we introduce the Creative Optimality Gap (COG), a novel +metric developed using causal-inferential machine learning methodologies to quantify the relative optimality of video +creatives. We provide an example application of this approach in assessing the effectiveness of video creatives for +brand advertising at Amazon. ### Ruomeng Cui, Emory University @@ -84,7 +97,7 @@ To be updated * Xinwei Ma, UC San Diego * [Zeyu Zheng](mailto:zyzheng@berkeley.edu), UC Berkeley, Amazon - main contact -### CausalML Team +### [CausalML](https://github.com/uber/causalml) Team * Jing Pan, Snap, CausalML * Yifeng Wu, Uber, CausalML @@ -94,7 +107,7 @@ To be updated * [Jeong-Yoon Lee](mailto:jeong@uber.com), Uber, CausalML - main contact * Zhenyu Zhao, Tencent, CausalML -### EconML Team +### [EconML](https://github.com/py-why/EconML) Team * Fabio Vera, Microsoft Research, EconML * Eleanor Dillon, Microsoft Research, EconML diff --git a/index.html b/index.html index e8457e2..1e9b395 100644 --- a/index.html +++ b/index.html @@ -14,22 +14,26 @@ ## **Abstract** -In recent years, both academic research and industry applications see an increased effort in using machine learning -methods to measure granular causal effects and design optimal policies based on these causal estimates. Open source -packages such as [CausalML](https://github.com/uber/causalml) and [EconML](https://github.com/microsoft/econml) provide -a unified interface for applied researchers and industry practitioners with a variety of machine learning methods for -causal inference. The tutorial will cover the topics including conditional treatment effect estimators by meta-learners -and tree-based algorithms, model validations and sensitivity analysis, optimization algorithms including policy leaner -and cost optimization. In addition, the tutorial will demonstrate the production of these algorithms in industry use -cases. - -## **Target Audience for the Workshop** - -Anyone who is interested in causal inference and machine learning, especially economists/statisticians/data scientists -who want to learn how to combine causal inference and machine learning with real industry use cases incorporated in -large scaled machine learning systems at companies such as Microsoft, TripAdvisor and Uber. -The tutorial assumes some basic knowledge in statistical methods, machine learning algorithms and the Python programming -language. +The increasing demand for data-driven decision-making has led to the rapid growth of machine learning applications in +various industries. However, the ability to draw causal inferences from observational data remains a crucial challenge. +In recent years, causal inference has emerged as a powerful tool for understanding the effects of interventions in +complex systems. Combining causal inference with machine learning has the potential to provide a deeper understanding of +the underlying mechanisms and to develop more effective solutions to real-world problems. + +This workshop aims to bring together researchers and practitioners from academia and industry to share their experiences +and insights on applying causal inference and machine learning techniques to real-world problems in the areas of +product, brand, policy, and beyond. The workshop welcomes original research that covers machine learning theory, deep +learning, causal inference, and online learning. Additionally, the workshop encourages topics that address scalable +system design, algorithm bias, and interpretablility. + +Through keynote talks, panel discussions, and contributed talks and posters, the workshop will provide a forum for +discussing the latest advances and challenges in applying causal inference and machine learning to real-world problems. +The workshop will also offer opportunities for networking and collaboration among researchers and practitioners working +in industry, government, and academia. + +## **Call for Paper** + +Please check the [Call for Paper](/cfp/) page for details on important dates and submission guidelines. ## **Outline** From 57ee61f2a286c751b5c337d327173fcc847532be Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Fri, 14 Apr 2023 20:13:03 -0700 Subject: [PATCH 14/33] style fix --- README.md | 4 ++-- index.html | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 7b97efc..fd1a3c0 100755 --- a/README.md +++ b/README.md @@ -8,8 +8,8 @@ ## **Schedule** -* Long Beach Convention & Entertainment Center, 300 E Ocean Blvd, Long Beach, CA 90802 ([Google -Map](https://goo.gl/maps/1N3XGEovGgJqXAV98)) +* Long Beach Convention & Entertainment Center, 300 E Ocean Blvd, Long Beach, CA 90802 +([Map](https://goo.gl/maps/1N3XGEovGgJqXAV98)) * 9:00 AM - 1:00 PM August 7, 2023 [PDT] ## **Abstract** diff --git a/index.html b/index.html index 1e9b395..fa5ff80 100644 --- a/index.html +++ b/index.html @@ -8,8 +8,8 @@ ## **Schedule** -* Long Beach Convention & Entertainment Center, 300 E Ocean Blvd, Long Beach, CA 90802 ([Google -Map](https://goo.gl/maps/1N3XGEovGgJqXAV98)) +* Long Beach Convention & Entertainment Center, 300 E Ocean Blvd, Long Beach, CA 90802 +([Map](https://goo.gl/maps/1N3XGEovGgJqXAV98)) * 9:00 AM - 1:00 PM August 7, 2023 [PDT] ## **Abstract** From ecbc739385072f72a04175a68a8814dd3a2692cd Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Sat, 15 Apr 2023 16:03:51 -0700 Subject: [PATCH 15/33] fix the cfp link in index.html --- README.md | 2 +- index.html | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index fd1a3c0..38a76a6 100755 --- a/README.md +++ b/README.md @@ -33,7 +33,7 @@ in industry, government, and academia. ## **Call for Paper** -Please check the [Call for Paper](/cfp/) page for details on important dates and submission guidelines. +Please check the [Call for Paper](https://causal-machine-learning.github.io/kdd2023-workshop/cfp/) page for details on important dates and submission guidelines. ## **Outline** diff --git a/index.html b/index.html index fa5ff80..c61776c 100644 --- a/index.html +++ b/index.html @@ -33,7 +33,8 @@ ## **Call for Paper** -Please check the [Call for Paper](/cfp/) page for details on important dates and submission guidelines. +Please check the [Call for Paper](https://causal-machine-learning.github.io/kdd2023-workshop/cfp/) page for details on +important dates and submission guidelines. ## **Outline** From 6195ea25295f62eea98f1448c29bbb2326fa5212 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Mon, 17 Apr 2023 08:44:30 -0700 Subject: [PATCH 16/33] update time to TBD. add Vasilis to keynote speakers --- README.md | 8 ++++++-- index.html | 8 ++++++-- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 38a76a6..ae043a7 100755 --- a/README.md +++ b/README.md @@ -10,7 +10,8 @@ * Long Beach Convention & Entertainment Center, 300 E Ocean Blvd, Long Beach, CA 90802 ([Map](https://goo.gl/maps/1N3XGEovGgJqXAV98)) -* 9:00 AM - 1:00 PM August 7, 2023 [PDT] +* Date: August 7, 2023 +* Time: TBD ## **Abstract** @@ -48,7 +49,7 @@ Please check the [Call for Paper](https://causal-machine-learning.github.io/kdd2 | Paper #4 | 15 minutes | | | Break & Poster Session | 30 minutes | | | Invited Talk #3 by Ang Li | 20 minutes | | -| Invited Talk #4 by TBD | 20 minutes | | +| Invited Talk #4 by Vasilis Syrgkanis | 20 minutes | | | Paper #1 | 15 minutes | | | Paper #2 | 15 minutes | | | Paper #3 | 15 minutes | | @@ -86,6 +87,9 @@ in the form of a causal diagram. We further discuss data availability issue rega derivation of the selection criterion without the observational or experimental data. We demonstrate the superiority of this criterion over A/B-test-based approaches. +### Vasilis Syrgkanis, Stanford University/EconML + + ## **Accepted Papers** To be updated diff --git a/index.html b/index.html index c61776c..d5e1fde 100644 --- a/index.html +++ b/index.html @@ -10,7 +10,8 @@ * Long Beach Convention & Entertainment Center, 300 E Ocean Blvd, Long Beach, CA 90802 ([Map](https://goo.gl/maps/1N3XGEovGgJqXAV98)) -* 9:00 AM - 1:00 PM August 7, 2023 [PDT] +* Date: August 7, 2023 +* Time: TBD ## **Abstract** @@ -49,7 +50,7 @@ | Paper #4 | 15 minutes | | | Break & Poster Session | 30 minutes | | | Invited Talk #3 by Ang Li | 20 minutes | | -| Invited Talk #4 by TBD | 20 minutes | | +| Invited Talk #4 by Vasilis Syrgkanis | 20 minutes | | | Paper #1 | 15 minutes | | | Paper #2 | 15 minutes | | | Paper #3 | 15 minutes | | @@ -87,6 +88,9 @@ derivation of the selection criterion without the observational or experimental data. 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__pycache__ diff --git a/README.md b/README.md index ae043a7..50459c8 100755 --- a/README.md +++ b/README.md @@ -25,56 +25,68 @@ This workshop aims to bring together researchers and practitioners from academia and insights on applying causal inference and machine learning techniques to real-world problems in the areas of product, brand, policy, and beyond. The workshop welcomes original research that covers machine learning theory, deep learning, causal inference, and online learning. Additionally, the workshop encourages topics that address scalable -system design, algorithm bias, and interpretablility. +system design, algorithm bias, and interpretability. Through keynote talks, panel discussions, and contributed talks and posters, the workshop will provide a forum for discussing the latest advances and challenges in applying causal inference and machine learning to real-world problems. The workshop will also offer opportunities for networking and collaboration among researchers and practitioners working in industry, government, and academia. -## **Call for Paper** +## **Paper Submission** -Please check the [Call for Paper](https://causal-machine-learning.github.io/kdd2023-workshop/cfp/) page for details on important dates and submission guidelines. +Please submit your paper to the [CMT portal](https://cmt3.research.microsoft.com/CMTSRM/) site, and check the [Call for Paper](https://causal-machine-learning.github.io/kdd2023-workshop/cfp/) page for details on important dates and submission guidelines. ## **Outline** -| **Title** | **Duration** | Link | -|-----------|--------------|--------| -| Introduction | 10 minutes | | -| Invited Talk #1 by Raif Rustamov | 20 minutes | | -| Invited Talk #2 by Ruomeng Cui | 20 minutes | | -| Paper #1 | 15 minutes | | -| Paper #2 | 15 minutes | | -| Paper #3 | 15 minutes | | -| Paper #4 | 15 minutes | | -| Break & Poster Session | 30 minutes | | -| Invited Talk #3 by Ang Li | 20 minutes | | -| Invited Talk #4 by Vasilis Syrgkanis | 20 minutes | | -| Paper #1 | 15 minutes | | -| Paper #2 | 15 minutes | | -| Paper #3 | 15 minutes | | -| Paper #4 | 15 minutes | | +| **Title** | **Speaker** | **Duration** | Link | +|-----------|-------------|--------------|------| +| Introduction | Organizers | 10 minutes | | +| Invited Talk #1 | [Raif Rustamov](#raif-rustamov-amazon) | 20 minutes | | +| Invited Talk: The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-universityamazon) | 20 minutes | | +| Paper #1 | | 15 minutes | | +| Paper #2 | | 15 minutes | | +| Paper #3 | | 15 minutes | | +| Paper #4 | | 15 minutes | | +| Break & Poster Session | | 30 minutes | | +| Invited Talk #3 | [Ang Li](#ang-li-university-of-california-los-angeles) | 20 minutes | | +| Invited Talk #4: Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 20 minutes | | +| Paper #1 | | 15 minutes | | +| Paper #2 | | 15 minutes | | +| Paper #3 | | 15 minutes | | +| Paper #4 | | 15 minutes | | ## **Invited Speakers** ### Raif Rustamov, Amazon +#### Bio + +![Raif Rustamov](/images/raif.png) + +Raif Rustamov is a Senior Applied Scientist at Amazon where he focuses on brand advertising science including relevance modeling, representation learning, and causal inference. He previously worked as a Principal Inventive Scientist in AI and Data Science at AT&T Labs conducting research on recommender systems, customer segmentation, identity for cross-device advertising, and location analytics. Raif has a PhD in Applied and Computational Mathematics from Princeton University and has taught at Purdue and Drew Universities, as well as worked as a research associate at Stanford University. + +#### Abstract + Video creatives have a substantial impact on consumer experiences and brand perceptions, but evaluating their effect on shopper behavior remains a complex challenge. In this talk, we introduce the Creative Optimality Gap (COG), a novel metric developed using causal-inferential machine learning methodologies to quantify the relative optimality of video creatives. We provide an example application of this approach in assessing the effectiveness of video creatives for brand advertising at Amazon. -### Ruomeng Cui, Emory University +### Ruomeng Cui, Emory University/Amazon + +#### Abstract -We will provide an overview of CausalML, an open source Python package that provides a suite of uplift modeling and -causal inference methods using machine learning algorithms based on recent research. We will introduce the main -components of CausalML: (1) inference with causal machine learning algorithms (e.g. meta-learners, uplift trees, CEVAE, -dragonnet), (2) validation/analysis methods (e.g. synthetic data generation, AUUC, sensitivity analysis, -interpretability), (3) optimization methods (e.g. policy optimization, value optimization, unit selection). +Last-mile delivery has become increasingly important in the online retail industry. In this study, we study the economic value of last-mile delivery. To do so, we conducted a quasi-experiment in collaboration with Cainiao, Alibaba's logistics subsidiary, where home delivery was launched at some pickup stations in 2021. This allowed us to comprehensively evaluate the causal impact of last-mile delivery. Using a difference-in-differences identification method, we found that last-mile delivery significantly increases sales and customer spending on the retail platform. To optimally prioritize limited delivery capacity, we employed causal machine learning to target the most responsive customers. Our findings suggest that online retailers should carefully weigh the costs and benefits of last-mile delivery and tailor their logistic strategies accordingly. ### Ang Li, University of California, Los Angeles +#### Bio + +Dr. Li is set to join the Florida State University Department of Computer Science as an assistant professor in August. He is currently a post-doctoral researcher in the Department of Computer Science at UCLA under the guidance of Prof. Judea Pearl. His primary research area is causal inference, artificial intelligence, and causality-based decision-making, with a focus on building causal models that estimate treatment effects (interventions) and evaluating what would have happened if an individual had taken a treatment (counterfactuals). He is also interested in decision-making modeling using knowledge of treatment effects and counterfactuals. Prior to his post-doc, Dr. Li obtained his Ph.D. at UCLA with Prof. Judea Pearl and his M.S. degree at the University of Minnesota Twin Cities. + +#### Abstract + The unit selection problem aims to identify a set of individuals who are most likely to exhibit a desired mode of behavior, which is defined in counterfactual terms. A typical example is that of selecting individuals who would respond one way if encouraged and a @@ -89,6 +101,11 @@ demonstrate the superiority of this criterion over A/B-test-based approaches. ### Vasilis Syrgkanis, Stanford University/EconML +#### Bio + +![Vasilis Syrgkanis](/images/vasilis.png) + +Vasilis Syrgkanis is an Assistant Professor in Management Science and Engineering and (by courtesy) in Computer Science, in the School of Engineering at Stanford University. His research interests are in the areas of machine learning, causal inference, econometrics, online and reinforcement learning, game theory/mechanism design and algorithm design. Until August 2022, he was a Principal Researcher at Microsoft Research, New England, where he was a member of the EconCS and StatsML groups. During his time at Microsoft, he co-led the project on Automated Learning and Intelligence for Causation and Economics (ALICE) and was a co-founder of EconML, an open-source python package for causal machine learning. He received his Ph.D. in Computer Science from Cornell University. ## **Accepted Papers** diff --git a/_pages/cfp.md b/_pages/cfp.md index 4d08a7c..e2c38c4 100644 --- a/_pages/cfp.md +++ b/_pages/cfp.md @@ -18,7 +18,7 @@ All deadlines are at 11:59 PM [AoE](https://www.timeanddate.com/time/zones/aoe). ## **Submission Link** -CMT submission portal will open on April 30th. +* CMT submission portal: [https://cmt3.research.microsoft.com/CMTSRM/Submission](https://cmt3.research.microsoft.com/CMTSRM/Submission) ## **Aim and Scope** diff --git a/images/raif.png b/images/raif.png new file mode 100644 index 0000000000000000000000000000000000000000..e627f586a621b3453ee26f1f281afc47ac0ec31f GIT binary patch literal 45626 zcmZ^}1ymi+vM;3Z`Pl1E^dySd0WCQ>ZMXklfm1V@m zNt9h2Ev)U#0RZW!R4rI-wGpfw-Q+|ODM8EEMp_$%K~*oK4+k#?lYt++H@WPGlWV@PfQDUy zBpO%^SQ4!t%<-S@OA?f1i@)bU5GVuS8G>OVvZzfYCBh)J!&kb}Jr{vw_SH*r4e!ez zhA}V+xkJ!^s1TmG^r4BcM*zSJNsD3||g!|*gazx;Q@CZ$E*VbqSBGpJ6!@0;Ji=Jca 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a/README.md b/README.md index 50459c8..0e0ff5c 100755 --- a/README.md +++ b/README.md @@ -61,8 +61,6 @@ Please submit your paper to the [CMT portal](https://cmt3.research.microsoft.com #### Bio -![Raif Rustamov](/images/raif.png) - Raif Rustamov is a Senior Applied Scientist at Amazon where he focuses on brand advertising science including relevance modeling, representation learning, and causal inference. He previously worked as a Principal Inventive Scientist in AI and Data Science at AT&T Labs conducting research on recommender systems, customer segmentation, identity for cross-device advertising, and location analytics. Raif has a PhD in Applied and Computational Mathematics from Princeton University and has taught at Purdue and Drew Universities, as well as worked as a research associate at Stanford University. #### Abstract @@ -103,8 +101,6 @@ demonstrate the superiority of this criterion over A/B-test-based approaches. #### Bio -![Vasilis Syrgkanis](/images/vasilis.png) - Vasilis Syrgkanis is an Assistant Professor in Management Science and Engineering and (by courtesy) in Computer Science, in the School of Engineering at Stanford University. His research interests are in the areas of machine learning, causal inference, econometrics, online and reinforcement learning, game theory/mechanism design and algorithm design. Until August 2022, he was a Principal Researcher at Microsoft Research, New England, where he was a member of the EconCS and StatsML groups. During his time at Microsoft, he co-led the project on Automated Learning and Intelligence for Causation and Economics (ALICE) and was a co-founder of EconML, an open-source python package for causal machine learning. He received his Ph.D. in Computer Science from Cornell University. ## **Accepted Papers** diff --git a/index.html b/index.html index 746df3d..08cdaa8 100644 --- a/index.html +++ b/index.html @@ -52,8 +52,7 @@ | Paper #4 | | 15 minutes | | | Break & Poster Session | | 30 minutes | | | Invited Talk #3 | [Ang Li](#ang-li-university-of-california-los-angeles) | 20 minutes | | -| Invited Talk #4: Towards Automating the Causal Machine Learning Pipeline | [Vasilis -Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 20 minutes | | +| Invited Talk #4: Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 20 minutes | | | Paper #1 | | 15 minutes | | | Paper #2 | | 15 minutes | | | Paper #3 | | 15 minutes | | @@ -65,8 +64,6 @@ #### Bio -![Raif Rustamov](/images/raif.png) - Raif Rustamov is a Senior Applied Scientist at Amazon where he focuses on brand advertising science including relevance modeling, representation learning, and causal inference. He previously worked as a Principal Inventive Scientist in AI and Data Science at AT&T Labs conducting research on recommender systems, customer segmentation, identity for @@ -125,8 +122,6 @@ #### Bio -![Vasilis Syrgkanis](/images/vasilis.png) - Vasilis Syrgkanis is an Assistant Professor in Management Science and Engineering and (by courtesy) in Computer Science, in the School of Engineering at Stanford University. His research interests are in the areas of machine learning, causal inference, econometrics, online and reinforcement learning, game theory/mechanism design and algorithm design. Until From aace389de9ca8549bdf82a9e9ecf382e92f72e31 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Mon, 1 May 2023 10:15:30 -0700 Subject: [PATCH 22/33] fix the submission link --- README.md | 8 ++++++-- _pages/cfp.md | 3 ++- index.html | 2 +- 3 files changed, 9 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 0e0ff5c..0d7106b 100755 --- a/README.md +++ b/README.md @@ -34,7 +34,7 @@ in industry, government, and academia. ## **Paper Submission** -Please submit your paper to the [CMT portal](https://cmt3.research.microsoft.com/CMTSRM/) site, and check the [Call for Paper](https://causal-machine-learning.github.io/kdd2023-workshop/cfp/) page for details on important dates and submission guidelines. +Please submit your paper to the [CMT portal](https://cmt3.research.microsoft.com/CMLKDD2023) site, and check the [Call for Paper](https://causal-machine-learning.github.io/kdd2023-workshop/cfp/) page for details on important dates and submission guidelines. ## **Outline** @@ -49,7 +49,7 @@ Please submit your paper to the [CMT portal](https://cmt3.research.microsoft.com | Paper #4 | | 15 minutes | | | Break & Poster Session | | 30 minutes | | | Invited Talk #3 | [Ang Li](#ang-li-university-of-california-los-angeles) | 20 minutes | | -| Invited Talk #4: Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 20 minutes | | +| Invited Talk #4: Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis | 20 minutes | | | Paper #1 | | 15 minutes | | | Paper #2 | | 15 minutes | | | Paper #3 | | 15 minutes | | @@ -61,6 +61,8 @@ Please submit your paper to the [CMT portal](https://cmt3.research.microsoft.com #### Bio +![Raif Rustamov](/images/raif.png) + Raif Rustamov is a Senior Applied Scientist at Amazon where he focuses on brand advertising science including relevance modeling, representation learning, and causal inference. He previously worked as a Principal Inventive Scientist in AI and Data Science at AT&T Labs conducting research on recommender systems, customer segmentation, identity for cross-device advertising, and location analytics. Raif has a PhD in Applied and Computational Mathematics from Princeton University and has taught at Purdue and Drew Universities, as well as worked as a research associate at Stanford University. #### Abstract @@ -101,6 +103,8 @@ demonstrate the superiority of this criterion over A/B-test-based approaches. #### Bio +![Vasilis Syrgkanis](/images/vasilis.png) + Vasilis Syrgkanis is an Assistant Professor in Management Science and Engineering and (by courtesy) in Computer Science, in the School of Engineering at Stanford University. His research interests are in the areas of machine learning, causal inference, econometrics, online and reinforcement learning, game theory/mechanism design and algorithm design. Until August 2022, he was a Principal Researcher at Microsoft Research, New England, where he was a member of the EconCS and StatsML groups. During his time at Microsoft, he co-led the project on Automated Learning and Intelligence for Causation and Economics (ALICE) and was a co-founder of EconML, an open-source python package for causal machine learning. He received his Ph.D. in Computer Science from Cornell University. ## **Accepted Papers** diff --git a/_pages/cfp.md b/_pages/cfp.md index e2c38c4..fbdd494 100644 --- a/_pages/cfp.md +++ b/_pages/cfp.md @@ -11,6 +11,7 @@ search_exclude: true All deadlines are at 11:59 PM [AoE](https://www.timeanddate.com/time/zones/aoe). * April 30th, 2023: CMT submission portal opens +* May 23rd, 2023: Abstract submission deadline * June 9th, 2023: Workshop paper submission deadline * July 10th, 2023: Paper decision notifications * July 24th, 2023: Camera-ready deadline @@ -18,7 +19,7 @@ All deadlines are at 11:59 PM [AoE](https://www.timeanddate.com/time/zones/aoe). ## **Submission Link** -* CMT submission portal: [https://cmt3.research.microsoft.com/CMTSRM/Submission](https://cmt3.research.microsoft.com/CMTSRM/Submission) +* CMT submission portal: [https://cmt3.research.microsoft.com/CMLKDD2023/](https://cmt3.research.microsoft.com/CMLKDD2023/) ## **Aim and Scope** diff --git a/index.html b/index.html index 08cdaa8..225aa07 100644 --- a/index.html +++ b/index.html @@ -34,7 +34,7 @@ ## **Paper Submission** -Please submit your paper to the [CMT portal](https://cmt3.research.microsoft.com/CMTSRM/) site, and check the [Call for +Please submit your paper to the [CMT portal](https://cmt3.research.microsoft.com/CMLKDD2023) site, and check the [Call for Paper](https://causal-machine-learning.github.io/kdd2023-workshop/cfp/) page for details on important dates and submission guidelines. From 3917f8e87a86b5945e01182e089df3830e3eef42 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Wed, 24 May 2023 12:22:19 -0700 Subject: [PATCH 23/33] extend the abstract submission deadline by a week --- _pages/cfp.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/_pages/cfp.md b/_pages/cfp.md index fbdd494..444425c 100644 --- a/_pages/cfp.md +++ b/_pages/cfp.md @@ -10,8 +10,8 @@ search_exclude: true ## **Important Dates** All deadlines are at 11:59 PM [AoE](https://www.timeanddate.com/time/zones/aoe). -* April 30th, 2023: CMT submission portal opens -* May 23rd, 2023: Abstract submission deadline +* ~~April 30th, 2023: CMT submission portal opens~~ +* ~~May 23rd, 2023~~ **May 30th, 2023**: Abstract submission deadline **(extended)** * June 9th, 2023: Workshop paper submission deadline * July 10th, 2023: Paper decision notifications * July 24th, 2023: Camera-ready deadline From 69e5a600edf0d7b895faf3fd0978d2bf0fbde517 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Mon, 12 Jun 2023 12:04:31 -0700 Subject: [PATCH 24/33] extended the paper submission deadline by a week --- _pages/cfp.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/_pages/cfp.md b/_pages/cfp.md index 444425c..d8f09e8 100644 --- a/_pages/cfp.md +++ b/_pages/cfp.md @@ -11,8 +11,8 @@ search_exclude: true All deadlines are at 11:59 PM [AoE](https://www.timeanddate.com/time/zones/aoe). * ~~April 30th, 2023: CMT submission portal opens~~ -* ~~May 23rd, 2023~~ **May 30th, 2023**: Abstract submission deadline **(extended)** -* June 9th, 2023: Workshop paper submission deadline +* ~~May 23rd, 2023 **May 30th, 2023**: Abstract submission deadline **(extended)**~~ +* ~~June 9th, 2023~~ **June 16th, 2023**: Workshop paper submission deadline **(extended)** * July 10th, 2023: Paper decision notifications * July 24th, 2023: Camera-ready deadline * August 7th, 2023: Workshop From 93478eef526d768ad8bc5165a362705bb9c79920 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Sat, 8 Jul 2023 01:17:32 -0700 Subject: [PATCH 25/33] update cfp. remove Raif's abstract per his request --- README.md | 8 -------- _pages/cfp.md | 2 +- index.html | 11 +---------- 3 files changed, 2 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index 0d7106b..1bd5a25 100755 --- a/README.md +++ b/README.md @@ -65,14 +65,6 @@ Please submit your paper to the [CMT portal](https://cmt3.research.microsoft.com Raif Rustamov is a Senior Applied Scientist at Amazon where he focuses on brand advertising science including relevance modeling, representation learning, and causal inference. He previously worked as a Principal Inventive Scientist in AI and Data Science at AT&T Labs conducting research on recommender systems, customer segmentation, identity for cross-device advertising, and location analytics. Raif has a PhD in Applied and Computational Mathematics from Princeton University and has taught at Purdue and Drew Universities, as well as worked as a research associate at Stanford University. -#### Abstract - -Video creatives have a substantial impact on consumer experiences and brand perceptions, but evaluating their effect on -shopper behavior remains a complex challenge. In this talk, we introduce the Creative Optimality Gap (COG), a novel -metric developed using causal-inferential machine learning methodologies to quantify the relative optimality of video -creatives. We provide an example application of this approach in assessing the effectiveness of video creatives for -brand advertising at Amazon. - ### Ruomeng Cui, Emory University/Amazon #### Abstract diff --git a/_pages/cfp.md b/_pages/cfp.md index d8f09e8..d117bc7 100644 --- a/_pages/cfp.md +++ b/_pages/cfp.md @@ -12,7 +12,7 @@ search_exclude: true All deadlines are at 11:59 PM [AoE](https://www.timeanddate.com/time/zones/aoe). * ~~April 30th, 2023: CMT submission portal opens~~ * ~~May 23rd, 2023 **May 30th, 2023**: Abstract submission deadline **(extended)**~~ -* ~~June 9th, 2023~~ **June 16th, 2023**: Workshop paper submission deadline **(extended)** +* ~~June 9th, 2023 **June 16th, 2023**: Workshop paper submission deadline **(extended)**~~ * July 10th, 2023: Paper decision notifications * July 24th, 2023: Camera-ready deadline * August 7th, 2023: Workshop diff --git a/index.html b/index.html index 225aa07..f7735e9 100644 --- a/index.html +++ b/index.html @@ -44,8 +44,7 @@ |-----------|-------------|--------------|------| | Introduction | Organizers | 10 minutes | | | Invited Talk #1 | [Raif Rustamov](#raif-rustamov-amazon) | 20 minutes | | -| Invited Talk: The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-universityamazon) | -20 minutes | | +| Invited Talk: The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-universityamazon) | 20 minutes | | | Paper #1 | | 15 minutes | | | Paper #2 | | 15 minutes | | | Paper #3 | | 15 minutes | | @@ -71,14 +70,6 @@ University and has taught at Purdue and Drew Universities, as well as worked as a research associate at Stanford University. -#### Abstract - -Video creatives have a substantial impact on consumer experiences and brand perceptions, but evaluating their effect on -shopper behavior remains a complex challenge. In this talk, we introduce the Creative Optimality Gap (COG), a novel -metric developed using causal-inferential machine learning methodologies to quantify the relative optimality of video -creatives. We provide an example application of this approach in assessing the effectiveness of video creatives for -brand advertising at Amazon. - ### Ruomeng Cui, Emory University/Amazon #### Abstract From f47c0fb9968b1e912a734bf54dfb9c00f0e50071 Mon Sep 17 00:00:00 2001 From: Totte Harinen <23464531+t-tte@users.noreply.github.com> Date: Tue, 18 Jul 2023 13:11:45 -0700 Subject: [PATCH 26/33] Update README.md --- README.md | 26 ++++++++++---------------- 1 file changed, 10 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index 1bd5a25..ffaa3ec 100755 --- a/README.md +++ b/README.md @@ -109,19 +109,13 @@ To be updated * Yingfei Wang, University of Washington * Xinwei Ma, UC San Diego * [Zeyu Zheng](mailto:zyzheng@berkeley.edu), UC Berkeley, Amazon - main contact - -### [CausalML](https://github.com/uber/causalml) Team - -* Jing Pan, Snap, CausalML -* Yifeng Wu, Uber, CausalML -* Huigang Chen, Meta, CausalML -* Totte Harinen, AirBnB, CausalML -* Paul Lo, Snap, CausalML -* [Jeong-Yoon Lee](mailto:jeong@uber.com), Uber, CausalML - main contact -* Zhenyu Zhao, Tencent, CausalML - -### [EconML](https://github.com/py-why/EconML) Team - -* Fabio Vera, Microsoft Research, EconML -* Eleanor Dillon, Microsoft Research, EconML -* Keith Battocchi, Microsoft Research, EconML +* Jing Pan, Snap, [CausalML](https://github.com/uber/causalml) +* Yifeng Wu, Uber, [CausalML](https://github.com/uber/causalml) +* Huigang Chen, Meta, [CausalML](https://github.com/uber/causalml) +* Totte Harinen, AirBnB, [CausalML](https://github.com/uber/causalml) +* Paul Lo, Snap, [CausalML](https://github.com/uber/causalml) +* [Jeong-Yoon Lee](mailto:jeong@uber.com), Uber, [CausalML](https://github.com/uber/causalml) - main contact +* Zhenyu Zhao, Tencent, [CausalML](https://github.com/uber/causalml) +* Fabio Vera, Microsoft Research, [EconML](https://github.com/py-why/EconML) +* Eleanor Dillon, Microsoft Research, [EconML](https://github.com/py-why/EconML) +* Keith Battocchi, Microsoft Research, [EconML](https://github.com/py-why/EconML) From cb0c7fac8c1ce30a0f14484ae502aa44082a0b45 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Wed, 26 Jul 2023 10:19:03 -0700 Subject: [PATCH 27/33] update accepted papers and invited talks --- README.md | 131 +++++++++++++++++++++++++++++++++++++++----------- _pages/cfp.md | 4 +- index.html | 61 ++++++++++++++++++----- 3 files changed, 154 insertions(+), 42 deletions(-) diff --git a/README.md b/README.md index ffaa3ec..0808fe8 100755 --- a/README.md +++ b/README.md @@ -4,6 +4,12 @@ ![](https://github.com/causal-machine-learning/kdd2023-workshop/workflows/GH-Pages%20Status/badge.svg) [![](https://img.shields.io/static/v1?label=fastai&message=fastpages&color=57aeac&labelColor=black&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABkAAAAjCAYAAABhCKGoAAAGMklEQVR42q1Xa0xTVxyfKExlui9blszoB12yDzPGzJhtyT5s+zBxUxELBQSHm2ZzU5epBF/LclXae29pCxR5VEGgLQUuIOKDuClhm8oUK7S9ve19tLTl/fA5p9MNc/Y/hRYEzGLxJL/87zk9Ob/zf5++NGHMALzYgdDYmWh0Qly3Lybtwi6lXdpN2cWN5A0+hrQKe5R2PoN2uD+OKcn/UF5ZsVduMmyXVRi+jzebdmI5/juhwrgj3mTI2GA0vvsUIcMwM7GkOD42t7Mf6bqHkFry2yk7X5PXcxMVDN5DGtFf9NkJfe6W5iaUyFShjfV1KPlk7VPAa0k11WjzL+eRvMJ4IKQO0dw8SydJL+Op0u5cn+3tQTn+fqTivTbQpiavF0iG7iGt6NevKjpKpTbUo3hj+QO47XB8hfHfIGAelA+T6mqQzFi+e0oTKm3iexQnXaU56ZrK5SlVsq70LMF7TuX0XNTyvi1rThzLST3TgOCgxwD0DPwDGoE07QkcSl/m5ynbHWmZVm6b0sp9o2DZN8aTZtqk9w9b2G2HLbbvsjlx+fry0vwU0OS5SH68Ylmilny3c3x9SOvpRuQN7hO8vqulZQ6WJMuXFAzcRfkDd5BG8B1bpc+nU0+fQtgkYLIngOEJwGt/J9UxCIJg1whJ05Ul4IMejbsLqUUfOjJKQnCDr4ySHMeO1/UMIa3UmR9TUpj7ZdMFJK8yo6RaZjLAF/JqM/rifCO+yP4AycGmlgUaT9cZ0OYP2um5prjBLhtvLhy68Fs7RFqbRvSlf15ybGdyLcPJmcpfIcIuT4nqqt+Sa2vaZaby1FB+JGi1c9INhuiv9fpIysItIh3CVgVAzXfEE1evzse/bwr8bolcAXs+zcqKXksQc5+FD2D/svT06I8IYtaUeZLZzsVm+3oRDmON1Ok/2NKyIJSs0xnj84RknXG6zgGEE1It+rsPtrYuDOxBKAJLrO1qnW7+OpqeNxF4HWv6v4Rql3uFRvL/DATnc/29x4lmy2t4fXVjY+ASGwylm8DBvkSm2gpgx1Bpg4hyyysqVoUuFRw0z8+jXe40yiFsp1lpC9navlJpE9JIh7RVwfJywmKZO4Hkh02NZ1FilfkJLi1B4GhLPduAZGazHO9LGDX/WAj7+npzwUQqvuOBoo1Va91dj3Tdgyinc0Dae+HyIrxvc2npbCxlxrJvcW3CeSKDMhKCoexRYnUlSqg0xU0iIS5dXwzm6c/x9iKKEx8q2lkV5RARJCcm9We2sgsZhGZmgMYjJOU7UhpOIqhRwwlmEwrBZHgCBRKkKX4ySVvbmzQnXoSDHWCyS6SV20Ha+VaSFTiSE8/ttVheDe4NarLxVB1kdE0fYAgjGaOWGYD1vxKrqmInkSBchRkmiuC4KILhonAo4+9gWVHYnElQMEsAxbRDSHtp7dq5CRWly2VlZe/EFRcvDcBQvBTPZeXly1JMpvlThzBBRASBoDsSBIpgOBQV6C+sUJzffwflQX8BTevCTZMZeoslUo9QJJZYTZDw3RuIKtIhlhXdfhDoJ7TTXY/XdBBpgUshwFMSRYTVwim7FJvt6aFyOnoVKqc7MZQDzzNwsmnd3UegCudl8R2qzHZ7bJbQoYGyn692+zMULCfXenoOacTOTBUnJYRFsq+5+a3sjp5BXM6hEz7ObHNoVEIHyocekiX6WIiykwWDd1HhzT8RzY2YqxnK0HNQBJtW500ddiwrDgdIeCABZ4MPnKQdk9xDhUP3wfHSqbBI9v/e9jo0Iy30cCOgAMyVgMMVCMwql/cQxfKp2R1dWWrRm0PzUkrIXC9ykDY+hnJ5DqkE709guriwSRgGzWTQCPABWJZ6vbNHQlgo099+CCEMPnF6xnwynYETEWd8ls0WPUpSWnTrfuAhAWacPslUiQRNLBGXFSA7TrL8V3gNhesTnLFY0jb+bYWVp0i7SClY184jVtcayi7so2yuA0r4npbjsV8CJHZhPQ7no323cJ5w8FqpLwR/YJNRnHs0hNGs6ZFw/Lpsb+9oj/dZSbuL0XUNojx4d9Gch5mOT0ImINsdKyHzT9Muz1lcXhRWbo9a8J3B72H8Lg6+bKb1hyWMPeERBXMGRxEBCM7Ddfh/1jDuWhb5+QkAAAAASUVORK5CYII=)](https://github.com/fastai/fastpages) +--- +layout: home +search_exclude: true +image: images/logo.png +--- + # **Causal Inference and Machine Learning in Practice**: Use cases for Product, Brand, Policy and Beyond ## **Schedule** @@ -34,48 +40,85 @@ in industry, government, and academia. ## **Paper Submission** -Please submit your paper to the [CMT portal](https://cmt3.research.microsoft.com/CMLKDD2023) site, and check the [Call for Paper](https://causal-machine-learning.github.io/kdd2023-workshop/cfp/) page for details on important dates and submission guidelines. +Please submit your paper to the [CMT portal](https://cmt3.research.microsoft.com/CMLKDD2023) site, and check the [Call for +Paper](https://causal-machine-learning.github.io/kdd2023-workshop/cfp/) page for details on important dates and +submission guidelines. ## **Outline** | **Title** | **Speaker** | **Duration** | Link | |-----------|-------------|--------------|------| | Introduction | Organizers | 10 minutes | | -| Invited Talk #1 | [Raif Rustamov](#raif-rustamov-amazon) | 20 minutes | | +| Invited Talk: COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) | 20 minutes | | | Invited Talk: The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-universityamazon) | 20 minutes | | -| Paper #1 | | 15 minutes | | -| Paper #2 | | 15 minutes | | -| Paper #3 | | 15 minutes | | -| Paper #4 | | 15 minutes | | -| Break & Poster Session | | 30 minutes | | +| Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation | Dmitri Goldenberg, Javier Albert (Booking.com) | 15 minutes | | +| Ensemble Method for Estimating Individualized Treatment Effects | Kevin Wu Han, Han Wu (Stanford) | 15 minutes | | +| A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 15 minutes | | +| An IPW-based Unbiased Ranking Metric in Two-sided Markets | Keisho Oh, Naoki Nishimura (Recruit Co), Minje Sung, Ken Kobayashi, Kazuhide Nakata (Tokyo Institute of Technology) | 15 minutes | | +| Break & Poster Session | | 30 minutes | | | Invited Talk #3 | [Ang Li](#ang-li-university-of-california-los-angeles) | 20 minutes | | -| Invited Talk #4: Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis | 20 minutes | | -| Paper #1 | | 15 minutes | | -| Paper #2 | | 15 minutes | | -| Paper #3 | | 15 minutes | | -| Paper #4 | | 15 minutes | | +| Invited Talk: Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 20 minutes | | +| Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments | Ali O Polat (Shipt) | 15 minutes | | +| Dynamic Causal Structure Discovery and Causal Effect Estimation | Jianian Wang, Rui Song (NCSU) | 15 minutes | | +| Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference | Yufei Wu, Zhiying Gu, Alex Deng, Jacob Zhu (Airbnb) | 15 minutes | | ## **Invited Speakers** ### Raif Rustamov, Amazon +#### Title: COG: Creative Optimality Gap for Video Advertising + #### Bio -![Raif Rustamov](/images/raif.png) +Raif Rustamov is a Senior Applied Scientist at Amazon where he focuses on brand advertising science including relevance +modeling, representation learning, and causal inference. He previously worked as a Principal Inventive Scientist in AI +and Data Science at AT&T Labs conducting research on recommender systems, customer segmentation, identity for +cross-device advertising, and location analytics. Raif has a PhD in Applied and Computational Mathematics from Princeton +University and has taught at Purdue and Drew Universities, as well as worked as a research associate at Stanford +University. -Raif Rustamov is a Senior Applied Scientist at Amazon where he focuses on brand advertising science including relevance modeling, representation learning, and causal inference. He previously worked as a Principal Inventive Scientist in AI and Data Science at AT&T Labs conducting research on recommender systems, customer segmentation, identity for cross-device advertising, and location analytics. Raif has a PhD in Applied and Computational Mathematics from Princeton University and has taught at Purdue and Drew Universities, as well as worked as a research associate at Stanford University. +#### Abstract + +Video creatives play a crucial role in shaping consumer experiences and brand perceptions, but quantifying their impact +on shopper experience remains a complex challenge. In this talk, we introduce the Creative Optimality Gap (COG), +a metric developed to assess the relative optimality of video creatives using causal-inferential machine learning +methodology. Our main contributions include the development of the COG metric through the use of conditional individual +treatment effects projected on interpretable video features, the introduction of a meta-learner for its computation, +and the incorporation of model uncertainty to avoid false positives. Our work advances the understanding of video +creative effectiveness and provides a valuable tool for optimizing ad performance. ### Ruomeng Cui, Emory University/Amazon +#### Title: The Value of Last-Mile Delivery in Online Retail + +#### Bio + +Ruomeng Cui is an Associate Professor of Operations Management at the Goizueta Business School, Emory University (on leave). She currently is a full-time Amazon Visiting Academic at Amazon, working in the supply chain domain. Her research focuses on causal inference, machine learning and data-driven modeling, with applications in retail, supply chains, and platforms. She currently serves as an associate editor for Manufacturing & Service Operations Management and Production and Operations Management. She received her Ph.D. in Operations Management from the Kellogg School of Management, Northwestern University and B.Sc in Industrial Engineering from Tsinghua University. + #### Abstract -Last-mile delivery has become increasingly important in the online retail industry. In this study, we study the economic value of last-mile delivery. To do so, we conducted a quasi-experiment in collaboration with Cainiao, Alibaba's logistics subsidiary, where home delivery was launched at some pickup stations in 2021. This allowed us to comprehensively evaluate the causal impact of last-mile delivery. Using a difference-in-differences identification method, we found that last-mile delivery significantly increases sales and customer spending on the retail platform. To optimally prioritize limited delivery capacity, we employed causal machine learning to target the most responsive customers. Our findings suggest that online retailers should carefully weigh the costs and benefits of last-mile delivery and tailor their logistic strategies accordingly. +Last-mile delivery has become increasingly important in the online retail industry. In this study, we study the economic +value of last-mile delivery. To do so, we conducted a quasi-experiment in collaboration with Cainiao, Alibaba's +logistics subsidiary, where home delivery was launched at some pickup stations in 2021. This allowed us to +comprehensively evaluate the causal impact of last-mile delivery. Using a difference-in-differences identification +method, we found that last-mile delivery significantly increases sales and customer spending on the retail platform. To +optimally prioritize limited delivery capacity, we employed causal machine learning to target the most responsive +customers. Our findings suggest that online retailers should carefully weigh the costs and benefits of last-mile +delivery and tailor their logistic strategies accordingly. ### Ang Li, University of California, Los Angeles +#### Title: + #### Bio -Dr. Li is set to join the Florida State University Department of Computer Science as an assistant professor in August. He is currently a post-doctoral researcher in the Department of Computer Science at UCLA under the guidance of Prof. Judea Pearl. His primary research area is causal inference, artificial intelligence, and causality-based decision-making, with a focus on building causal models that estimate treatment effects (interventions) and evaluating what would have happened if an individual had taken a treatment (counterfactuals). He is also interested in decision-making modeling using knowledge of treatment effects and counterfactuals. Prior to his post-doc, Dr. Li obtained his Ph.D. at UCLA with Prof. Judea Pearl and his M.S. degree at the University of Minnesota Twin Cities. +Dr. Li is set to join the Florida State University Department of Computer Science as an assistant professor in August. +He is currently a post-doctoral researcher in the Department of Computer Science at UCLA under the guidance of Prof. +Judea Pearl. His primary research area is causal inference, artificial intelligence, and causality-based +decision-making, with a focus on building causal models that estimate treatment effects (interventions) and evaluating +what would have happened if an individual had taken a treatment (counterfactuals). He is also interested in +decision-making modeling using knowledge of treatment effects and counterfactuals. Prior to his post-doc, Dr. Li +obtained his Ph.D. at UCLA with Prof. Judea Pearl and his M.S. degree at the University of Minnesota Twin Cities. #### Abstract @@ -93,15 +136,39 @@ demonstrate the superiority of this criterion over A/B-test-based approaches. ### Vasilis Syrgkanis, Stanford University/EconML +#### Title: Towards Automating the Causal Machine Learning Pipeline + #### Bio -![Vasilis Syrgkanis](/images/vasilis.png) +Vasilis Syrgkanis is an Assistant Professor in Management Science and Engineering and (by courtesy) in Computer Science, +in the School of Engineering at Stanford University. His research interests are in the areas of machine learning, causal +inference, econometrics, online and reinforcement learning, game theory/mechanism design and algorithm design. Until +August 2022, he was a Principal Researcher at Microsoft Research, New England, where he was a member of the EconCS and +StatsML groups. During his time at Microsoft, he co-led the project on Automated Learning and Intelligence for Causation +and Economics (ALICE) and was a co-founder of EconML, an open-source python package for causal machine learning. He +received his Ph.D. in Computer Science from Cornell University. -Vasilis Syrgkanis is an Assistant Professor in Management Science and Engineering and (by courtesy) in Computer Science, in the School of Engineering at Stanford University. His research interests are in the areas of machine learning, causal inference, econometrics, online and reinforcement learning, game theory/mechanism design and algorithm design. Until August 2022, he was a Principal Researcher at Microsoft Research, New England, where he was a member of the EconCS and StatsML groups. During his time at Microsoft, he co-led the project on Automated Learning and Intelligence for Causation and Economics (ALICE) and was a co-founder of EconML, an open-source python package for causal machine learning. He received his Ph.D. in Computer Science from Cornell University. +#### Abstract ## **Accepted Papers** -To be updated +### For Oral Presentation +1. Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation, Dmitri Goldenberg (Booking.com)*; Javier Albert (Booking.com) +2. Ensemble Method for Estimating Individualized Treatment Effects, Kevin Wu Han (Stanford University)*; Han Wu (Stanford University) +3. A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization, Nicolò Cosimo Albanese (AWS)*; Fabian Furrer (AWS); Marco Guerriero (AWS) +4. An IPW-based Unbiased Ranking Metric in Two-sided Markets, Keisho Oh (Recruit Co., Ltd.)*; Naoki Nishimura (Recruit Co., Ltd.); Minje Sung (Tokyo Institute of Technology); Ken Kobayashi (Tokyo Institute of Technology); Kazuhide Nakata (Department of Industrial Engineering and Economics, Tokyo Institute of Technology.) +5. Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments, Ali O Polat (Shipt Inc.)* +6. Dynamic Causal Structure Discovery and Causal Effect Estimation, Jianian Wang (North Carolina State Unicersity)*; Rui Song (North Carolina State Unicersity) +7. Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference, Yufei Wu (Airbnb)*; Zhiying Gu (Airbnb); Alex Deng (Airbnb); Jacob Zhu (Airbnb) + +### For Poster Presentation +8. Community Detection-Enhanced Causal Structural Learning, Yuhe Gao (North Carolina State University)*; Hengrui Cai (University of California Irvine); Sheng Zhang (North Carolina State University); Rui Song (North Carolina State University) +9. ACE: Active Learning for Causal Inference with Expensive Experiments, Difan Song (Georgia Institute of Technology)*; Simon Mak (Duke University); C.F. Jeff Wu (Georgia Institute of Technology) +10. Extracting Causal Insights from Microsoft Feedback Hub using LLMs and In-context Learning, Sara Abdali (University of California, Riverside )*; Anjali Parikh (Microsoft); Steve Lim (Microsoft) +11. Evaluate the Impact of Similar Products Ad Group Recommendations with Causal Inference, Jamie Chen (Amazon)*; Zuqi Shang (AmaOn); Raif Rustamov (Amazon) +12. Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing, Shahin Boluki (Pros Inc)*; Ravi Kumar (PROS) +13. OpportunityFinder: A Framework for Automated Causal Inference, Huy Nguyen (Amazon)*; Prince Grover (Amazon); Devashish Khatwani (Amazon) + ## **Organizers** @@ -109,13 +176,19 @@ To be updated * Yingfei Wang, University of Washington * Xinwei Ma, UC San Diego * [Zeyu Zheng](mailto:zyzheng@berkeley.edu), UC Berkeley, Amazon - main contact -* Jing Pan, Snap, [CausalML](https://github.com/uber/causalml) -* Yifeng Wu, Uber, [CausalML](https://github.com/uber/causalml) -* Huigang Chen, Meta, [CausalML](https://github.com/uber/causalml) -* Totte Harinen, AirBnB, [CausalML](https://github.com/uber/causalml) -* Paul Lo, Snap, [CausalML](https://github.com/uber/causalml) -* [Jeong-Yoon Lee](mailto:jeong@uber.com), Uber, [CausalML](https://github.com/uber/causalml) - main contact -* Zhenyu Zhao, Tencent, [CausalML](https://github.com/uber/causalml) -* Fabio Vera, Microsoft Research, [EconML](https://github.com/py-why/EconML) -* Eleanor Dillon, Microsoft Research, [EconML](https://github.com/py-why/EconML) -* Keith Battocchi, Microsoft Research, [EconML](https://github.com/py-why/EconML) + +### [CausalML](https://github.com/uber/causalml) Team + +* Jing Pan, Snap, CausalML +* Yifeng Wu, Uber, CausalML +* Huigang Chen, Meta, CausalML +* Totte Harinen, AirBnB, CausalML +* Paul Lo, Snap, CausalML +* [Jeong-Yoon Lee](mailto:jeong@uber.com), Uber, CausalML - main contact +* Zhenyu Zhao, Tencent, CausalML + +### [EconML](https://github.com/py-why/EconML) Team + +* Fabio Vera, Microsoft Research, EconML +* Eleanor Dillon, Microsoft Research, EconML +* Keith Battocchi, Microsoft Research, EconML \ No newline at end of file diff --git a/_pages/cfp.md b/_pages/cfp.md index d117bc7..451a37e 100644 --- a/_pages/cfp.md +++ b/_pages/cfp.md @@ -13,8 +13,8 @@ All deadlines are at 11:59 PM [AoE](https://www.timeanddate.com/time/zones/aoe). * ~~April 30th, 2023: CMT submission portal opens~~ * ~~May 23rd, 2023 **May 30th, 2023**: Abstract submission deadline **(extended)**~~ * ~~June 9th, 2023 **June 16th, 2023**: Workshop paper submission deadline **(extended)**~~ -* July 10th, 2023: Paper decision notifications -* July 24th, 2023: Camera-ready deadline +* ~~July 10th, 2023: Paper decision notifications~~ +* ~~July 24th, 2023: Camera-ready deadline~~ * August 7th, 2023: Workshop ## **Submission Link** diff --git a/index.html b/index.html index f7735e9..9515400 100644 --- a/index.html +++ b/index.html @@ -43,24 +43,25 @@ | **Title** | **Speaker** | **Duration** | Link | |-----------|-------------|--------------|------| | Introduction | Organizers | 10 minutes | | -| Invited Talk #1 | [Raif Rustamov](#raif-rustamov-amazon) | 20 minutes | | +| Invited Talk: COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) | 20 minutes | | | Invited Talk: The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-universityamazon) | 20 minutes | | -| Paper #1 | | 15 minutes | | -| Paper #2 | | 15 minutes | | -| Paper #3 | | 15 minutes | | -| Paper #4 | | 15 minutes | | +| Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation | Dmitri Goldenberg, Javier Albert (Booking.com) | 15 minutes | | +| Ensemble Method for Estimating Individualized Treatment Effects | Kevin Wu Han, Han Wu (Stanford) | 15 minutes | | +| A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 15 minutes | | +| An IPW-based Unbiased Ranking Metric in Two-sided Markets | Keisho Oh, Naoki Nishimura (Recruit Co), Minje Sung, Ken Kobayashi, Kazuhide Nakata (Tokyo Institute of Technology) | 15 minutes | | | Break & Poster Session | | 30 minutes | | | Invited Talk #3 | [Ang Li](#ang-li-university-of-california-los-angeles) | 20 minutes | | -| Invited Talk #4: Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 20 minutes | | -| Paper #1 | | 15 minutes | | -| Paper #2 | | 15 minutes | | -| Paper #3 | | 15 minutes | | -| Paper #4 | | 15 minutes | | +| Invited Talk: Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 20 minutes | | +| Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments | Ali O Polat (Shipt) | 15 minutes | | +| Dynamic Causal Structure Discovery and Causal Effect Estimation | Jianian Wang, Rui Song (NCSU) | 15 minutes | | +| Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference | Yufei Wu, Zhiying Gu, Alex Deng, Jacob Zhu (Airbnb) | 15 minutes | | ## **Invited Speakers** ### Raif Rustamov, Amazon +#### Title: COG: Creative Optimality Gap for Video Advertising + #### Bio Raif Rustamov is a Senior Applied Scientist at Amazon where he focuses on brand advertising science including relevance @@ -70,8 +71,24 @@ University and has taught at Purdue and Drew Universities, as well as worked as a research associate at Stanford University. +#### Abstract + +Video creatives play a crucial role in shaping consumer experiences and brand perceptions, but quantifying their impact +on shopper experience remains a complex challenge. In this talk, we introduce the Creative Optimality Gap (COG), +a metric developed to assess the relative optimality of video creatives using causal-inferential machine learning +methodology. Our main contributions include the development of the COG metric through the use of conditional individual +treatment effects projected on interpretable video features, the introduction of a meta-learner for its computation, +and the incorporation of model uncertainty to avoid false positives. Our work advances the understanding of video +creative effectiveness and provides a valuable tool for optimizing ad performance. + ### Ruomeng Cui, Emory University/Amazon +#### Title: The Value of Last-Mile Delivery in Online Retail + +#### Bio + +Ruomeng Cui is an Associate Professor of Operations Management at the Goizueta Business School, Emory University (on leave). She currently is a full-time Amazon Visiting Academic at Amazon, working in the supply chain domain. Her research focuses on causal inference, machine learning and data-driven modeling, with applications in retail, supply chains, and platforms. She currently serves as an associate editor for Manufacturing & Service Operations Management and Production and Operations Management. She received her Ph.D. in Operations Management from the Kellogg School of Management, Northwestern University and B.Sc in Industrial Engineering from Tsinghua University. + #### Abstract Last-mile delivery has become increasingly important in the online retail industry. In this study, we study the economic @@ -85,6 +102,8 @@ ### Ang Li, University of California, Los Angeles +#### Title: + #### Bio Dr. Li is set to join the Florida State University Department of Computer Science as an assistant professor in August. @@ -111,6 +130,8 @@ ### Vasilis Syrgkanis, Stanford University/EconML +#### Title: Towards Automating the Causal Machine Learning Pipeline + #### Bio Vasilis Syrgkanis is an Assistant Professor in Management Science and Engineering and (by courtesy) in Computer Science, @@ -121,9 +142,27 @@ and Economics (ALICE) and was a co-founder of EconML, an open-source python package for causal machine learning. He received his Ph.D. in Computer Science from Cornell University. +#### Abstract + ## **Accepted Papers** -To be updated +### For Oral Presentation +1. Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation, Dmitri Goldenberg (Booking.com)*; Javier Albert (Booking.com) +2. Ensemble Method for Estimating Individualized Treatment Effects, Kevin Wu Han (Stanford University)*; Han Wu (Stanford University) +3. A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization, Nicolò Cosimo Albanese (AWS)*; Fabian Furrer (AWS); Marco Guerriero (AWS) +4. An IPW-based Unbiased Ranking Metric in Two-sided Markets, Keisho Oh (Recruit Co., Ltd.)*; Naoki Nishimura (Recruit Co., Ltd.); Minje Sung (Tokyo Institute of Technology); Ken Kobayashi (Tokyo Institute of Technology); Kazuhide Nakata (Department of Industrial Engineering and Economics, Tokyo Institute of Technology.) +5. Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments, Ali O Polat (Shipt Inc.)* +6. Dynamic Causal Structure Discovery and Causal Effect Estimation, Jianian Wang (North Carolina State Unicersity)*; Rui Song (North Carolina State Unicersity) +7. Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference, Yufei Wu (Airbnb)*; Zhiying Gu (Airbnb); Alex Deng (Airbnb); Jacob Zhu (Airbnb) + +### For Poster Presentation +8. Community Detection-Enhanced Causal Structural Learning, Yuhe Gao (North Carolina State University)*; Hengrui Cai (University of California Irvine); Sheng Zhang (North Carolina State University); Rui Song (North Carolina State University) +9. ACE: Active Learning for Causal Inference with Expensive Experiments, Difan Song (Georgia Institute of Technology)*; Simon Mak (Duke University); C.F. Jeff Wu (Georgia Institute of Technology) +10. Extracting Causal Insights from Microsoft Feedback Hub using LLMs and In-context Learning, Sara Abdali (University of California, Riverside )*; Anjali Parikh (Microsoft); Steve Lim (Microsoft) +11. Evaluate the Impact of Similar Products Ad Group Recommendations with Causal Inference, Jamie Chen (Amazon)*; Zuqi Shang (AmaOn); Raif Rustamov (Amazon) +12. Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing, Shahin Boluki (Pros Inc)*; Ravi Kumar (PROS) +13. OpportunityFinder: A Framework for Automated Causal Inference, Huy Nguyen (Amazon)*; Prince Grover (Amazon); Devashish Khatwani (Amazon) + ## **Organizers** From dde7313e2acbf3b5f98fda3a5c036abc58bee227 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Wed, 26 Jul 2023 10:24:48 -0700 Subject: [PATCH 28/33] fix formatting --- README.md | 16 ++++++++-------- index.html | 16 ++++++++-------- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index 0808fe8..d83c84a 100755 --- a/README.md +++ b/README.md @@ -16,8 +16,8 @@ image: images/logo.png * Long Beach Convention & Entertainment Center, 300 E Ocean Blvd, Long Beach, CA 90802 ([Map](https://goo.gl/maps/1N3XGEovGgJqXAV98)) -* Date: August 7, 2023 -* Time: TBD +* Date: August 7, 2023 (Monday) +* Time: TBD (PM) ## **Abstract** @@ -48,16 +48,16 @@ submission guidelines. | **Title** | **Speaker** | **Duration** | Link | |-----------|-------------|--------------|------| -| Introduction | Organizers | 10 minutes | | -| Invited Talk: COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) | 20 minutes | | -| Invited Talk: The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-universityamazon) | 20 minutes | | +| **Introduction** | Organizers | 10 minutes | | +| **Invited Talk:** COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) | 20 minutes | | +| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-universityamazon) | 20 minutes | | | Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation | Dmitri Goldenberg, Javier Albert (Booking.com) | 15 minutes | | | Ensemble Method for Estimating Individualized Treatment Effects | Kevin Wu Han, Han Wu (Stanford) | 15 minutes | | | A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 15 minutes | | | An IPW-based Unbiased Ranking Metric in Two-sided Markets | Keisho Oh, Naoki Nishimura (Recruit Co), Minje Sung, Ken Kobayashi, Kazuhide Nakata (Tokyo Institute of Technology) | 15 minutes | | -| Break & Poster Session | | 30 minutes | | -| Invited Talk #3 | [Ang Li](#ang-li-university-of-california-los-angeles) | 20 minutes | | -| Invited Talk: Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 20 minutes | | +| **Break & Poster Session** | | 30 minutes | | +| **Invited Talk:** | [Ang Li](#ang-li-university-of-california-los-angeles) | 20 minutes | | +| **Invited Talk:** Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 20 minutes | | | Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments | Ali O Polat (Shipt) | 15 minutes | | | Dynamic Causal Structure Discovery and Causal Effect Estimation | Jianian Wang, Rui Song (NCSU) | 15 minutes | | | Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference | Yufei Wu, Zhiying Gu, Alex Deng, Jacob Zhu (Airbnb) | 15 minutes | | diff --git a/index.html b/index.html index 9515400..3127650 100644 --- a/index.html +++ b/index.html @@ -10,8 +10,8 @@ * Long Beach Convention & Entertainment Center, 300 E Ocean Blvd, Long Beach, CA 90802 ([Map](https://goo.gl/maps/1N3XGEovGgJqXAV98)) -* Date: August 7, 2023 -* Time: TBD +* Date: August 7, 2023 (Monday) +* Time: TBD (PM) ## **Abstract** @@ -42,16 +42,16 @@ | **Title** | **Speaker** | **Duration** | Link | |-----------|-------------|--------------|------| -| Introduction | Organizers | 10 minutes | | -| Invited Talk: COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) | 20 minutes | | -| Invited Talk: The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-universityamazon) | 20 minutes | | +| **Introduction** | Organizers | 10 minutes | | +| **Invited Talk:** COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) | 20 minutes | | +| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-universityamazon) | 20 minutes | | | Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation | Dmitri Goldenberg, Javier Albert (Booking.com) | 15 minutes | | | Ensemble Method for Estimating Individualized Treatment Effects | Kevin Wu Han, Han Wu (Stanford) | 15 minutes | | | A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 15 minutes | | | An IPW-based Unbiased Ranking Metric in Two-sided Markets | Keisho Oh, Naoki Nishimura (Recruit Co), Minje Sung, Ken Kobayashi, Kazuhide Nakata (Tokyo Institute of Technology) | 15 minutes | | -| Break & Poster Session | | 30 minutes | | -| Invited Talk #3 | [Ang Li](#ang-li-university-of-california-los-angeles) | 20 minutes | | -| Invited Talk: Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 20 minutes | | +| **Break & Poster Session** | | 30 minutes | | +| **Invited Talk:** | [Ang Li](#ang-li-university-of-california-los-angeles) | 20 minutes | | +| **Invited Talk:** Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 20 minutes | | | Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments | Ali O Polat (Shipt) | 15 minutes | | | Dynamic Causal Structure Discovery and Causal Effect Estimation | Jianian Wang, Rui Song (NCSU) | 15 minutes | | | Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference | Yufei Wu, Zhiying Gu, Alex Deng, Jacob Zhu (Airbnb) | 15 minutes | | From 27a311b7013e2efffb9387c0394a12ebae7006c7 Mon Sep 17 00:00:00 2001 From: paullo0106 Date: Mon, 31 Jul 2023 16:06:25 -0700 Subject: [PATCH 29/33] Update author's bio --- README.md | 6 +++--- index.html | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index d83c84a..e9a0e31 100755 --- a/README.md +++ b/README.md @@ -50,7 +50,7 @@ submission guidelines. |-----------|-------------|--------------|------| | **Introduction** | Organizers | 10 minutes | | | **Invited Talk:** COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) | 20 minutes | | -| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-universityamazon) | 20 minutes | | +| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-university) | 20 minutes | | | Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation | Dmitri Goldenberg, Javier Albert (Booking.com) | 15 minutes | | | Ensemble Method for Estimating Individualized Treatment Effects | Kevin Wu Han, Han Wu (Stanford) | 15 minutes | | | A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 15 minutes | | @@ -87,7 +87,7 @@ treatment effects projected on interpretable video features, the introduction of and the incorporation of model uncertainty to avoid false positives. Our work advances the understanding of video creative effectiveness and provides a valuable tool for optimizing ad performance. -### Ruomeng Cui, Emory University/Amazon +### Ruomeng Cui, Emory University #### Title: The Value of Last-Mile Delivery in Online Retail @@ -191,4 +191,4 @@ received his Ph.D. in Computer Science from Cornell University. * Fabio Vera, Microsoft Research, EconML * Eleanor Dillon, Microsoft Research, EconML -* Keith Battocchi, Microsoft Research, EconML \ No newline at end of file +* Keith Battocchi, Microsoft Research, EconML diff --git a/index.html b/index.html index 3127650..5d6d998 100644 --- a/index.html +++ b/index.html @@ -44,7 +44,7 @@ |-----------|-------------|--------------|------| | **Introduction** | Organizers | 10 minutes | | | **Invited Talk:** COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) | 20 minutes | | -| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-universityamazon) | 20 minutes | | +| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-university) | 20 minutes | | | Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation | Dmitri Goldenberg, Javier Albert (Booking.com) | 15 minutes | | | Ensemble Method for Estimating Individualized Treatment Effects | Kevin Wu Han, Han Wu (Stanford) | 15 minutes | | | A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 15 minutes | | @@ -81,7 +81,7 @@ and the incorporation of model uncertainty to avoid false positives. Our work advances the understanding of video creative effectiveness and provides a valuable tool for optimizing ad performance. -### Ruomeng Cui, Emory University/Amazon +### Ruomeng Cui, Emory University #### Title: The Value of Last-Mile Delivery in Online Retail @@ -185,4 +185,4 @@ * Fabio Vera, Microsoft Research, EconML * Eleanor Dillon, Microsoft Research, EconML -* Keith Battocchi, Microsoft Research, EconML \ No newline at end of file +* Keith Battocchi, Microsoft Research, EconML From 67a85c25c5acc7675aa1d3224e39ac3215da42e3 Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Wed, 2 Aug 2023 15:26:28 -0700 Subject: [PATCH 30/33] add the title for Ang Li's invited talk --- README.md | 4 ++-- index.html | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index e9a0e31..f3bda66 100755 --- a/README.md +++ b/README.md @@ -56,7 +56,7 @@ submission guidelines. | A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 15 minutes | | | An IPW-based Unbiased Ranking Metric in Two-sided Markets | Keisho Oh, Naoki Nishimura (Recruit Co), Minje Sung, Ken Kobayashi, Kazuhide Nakata (Tokyo Institute of Technology) | 15 minutes | | | **Break & Poster Session** | | 30 minutes | | -| **Invited Talk:** | [Ang Li](#ang-li-university-of-california-los-angeles) | 20 minutes | | +| **Invited Talk:** Unit Selection Based on Counterfactual Logic | [Ang Li](#ang-li-university-of-california-los-angeles) | 20 minutes | | | **Invited Talk:** Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 20 minutes | | | Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments | Ali O Polat (Shipt) | 15 minutes | | | Dynamic Causal Structure Discovery and Causal Effect Estimation | Jianian Wang, Rui Song (NCSU) | 15 minutes | | @@ -108,7 +108,7 @@ delivery and tailor their logistic strategies accordingly. ### Ang Li, University of California, Los Angeles -#### Title: +#### Title: Unit Selection Based on Counterfactual Logic #### Bio diff --git a/index.html b/index.html index 5d6d998..bba5356 100644 --- a/index.html +++ b/index.html @@ -50,7 +50,7 @@ | A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 15 minutes | | | An IPW-based Unbiased Ranking Metric in Two-sided Markets | Keisho Oh, Naoki Nishimura (Recruit Co), Minje Sung, Ken Kobayashi, Kazuhide Nakata (Tokyo Institute of Technology) | 15 minutes | | | **Break & Poster Session** | | 30 minutes | | -| **Invited Talk:** | [Ang Li](#ang-li-university-of-california-los-angeles) | 20 minutes | | +| **Invited Talk:** Unit Selection Based on Counterfactual Logic | [Ang Li](#ang-li-university-of-california-los-angeles) | 20 minutes | | | **Invited Talk:** Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 20 minutes | | | Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments | Ali O Polat (Shipt) | 15 minutes | | | Dynamic Causal Structure Discovery and Causal Effect Estimation | Jianian Wang, Rui Song (NCSU) | 15 minutes | | @@ -102,7 +102,7 @@ ### Ang Li, University of California, Los Angeles -#### Title: +#### Title: Unit Selection Based on Counterfactual Logic #### Bio From 427fc82442bcb14c34a9c6ff78c52e2ac96476da Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Fri, 4 Aug 2023 09:54:54 -0700 Subject: [PATCH 31/33] update time and schedule --- README.md | 30 +++++++++++++++--------------- index.html | 30 +++++++++++++++--------------- 2 files changed, 30 insertions(+), 30 deletions(-) diff --git a/README.md b/README.md index f3bda66..995b304 100755 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ image: images/logo.png * Long Beach Convention & Entertainment Center, 300 E Ocean Blvd, Long Beach, CA 90802 ([Map](https://goo.gl/maps/1N3XGEovGgJqXAV98)) * Date: August 7, 2023 (Monday) -* Time: TBD (PM) +* Time: 1:00 - 5:00 PM Pacific Time ## **Abstract** @@ -46,21 +46,21 @@ submission guidelines. ## **Outline** -| **Title** | **Speaker** | **Duration** | Link | +| **Title** | **Speaker** | **Time (Duration)** | Link | |-----------|-------------|--------------|------| -| **Introduction** | Organizers | 10 minutes | | -| **Invited Talk:** COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) | 20 minutes | | -| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-university) | 20 minutes | | -| Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation | Dmitri Goldenberg, Javier Albert (Booking.com) | 15 minutes | | -| Ensemble Method for Estimating Individualized Treatment Effects | Kevin Wu Han, Han Wu (Stanford) | 15 minutes | | -| A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 15 minutes | | -| An IPW-based Unbiased Ranking Metric in Two-sided Markets | Keisho Oh, Naoki Nishimura (Recruit Co), Minje Sung, Ken Kobayashi, Kazuhide Nakata (Tokyo Institute of Technology) | 15 minutes | | -| **Break & Poster Session** | | 30 minutes | | -| **Invited Talk:** Unit Selection Based on Counterfactual Logic | [Ang Li](#ang-li-university-of-california-los-angeles) | 20 minutes | | -| **Invited Talk:** Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 20 minutes | | -| Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments | Ali O Polat (Shipt) | 15 minutes | | -| Dynamic Causal Structure Discovery and Causal Effect Estimation | Jianian Wang, Rui Song (NCSU) | 15 minutes | | -| Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference | Yufei Wu, Zhiying Gu, Alex Deng, Jacob Zhu (Airbnb) | 15 minutes | | +| **Introduction** | Organizers | 1:00 - 1:10 PM (10 minutes) | | +| **Invited Talk:** COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) | 1:10 - 1:30 PM (20 minutes) | | +| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-university) | 1:30 - 1:50 PM (20 minutes) | | +| Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation | Dmitri Goldenberg, Javier Albert (Booking.com) | 1:50 - 2:05 PM (15 minutes) | | +| Ensemble Method for Estimating Individualized Treatment Effects | Kevin Wu Han, Han Wu (Stanford) | 2:05 - 2:20 PM (15 minutes) | | +| A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 2:20 - 2:35 PM (15 minutes) | | +| An IPW-based Unbiased Ranking Metric in Two-sided Markets | Keisho Oh, Naoki Nishimura (Recruit Co), Minje Sung, Ken Kobayashi, Kazuhide Nakata (Tokyo Institute of Technology) | 2:35 - 2:50 PM (15 minutes) | | +| **Break & Poster Session** | | 3:00 - 3:30 PM (30 minutes) | | +| **Invited Talk:** Unit Selection Based on Counterfactual Logic | [Ang Li](#ang-li-university-of-california-los-angeles) | 3:30 - 3:50 PM (20 minutes) | | +| **Invited Talk:** Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 3:50 - 4:10 PM (20 minutes) | | +| Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments | Ali O Polat (Shipt) | 4:10 - 4:25 PM (15 minutes) | | +| Dynamic Causal Structure Discovery and Causal Effect Estimation | Jianian Wang, Rui Song (NCSU) | 4:25 - 4:40 PM (15 minutes) | | +| Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference | Yufei Wu, Zhiying Gu, Alex Deng, Jacob Zhu (Airbnb) | 4:40 - 4:55 PM (15 minutes) | | ## **Invited Speakers** diff --git a/index.html b/index.html index bba5356..a54eb91 100644 --- a/index.html +++ b/index.html @@ -11,7 +11,7 @@ * Long Beach Convention & Entertainment Center, 300 E Ocean Blvd, Long Beach, CA 90802 ([Map](https://goo.gl/maps/1N3XGEovGgJqXAV98)) * Date: August 7, 2023 (Monday) -* Time: TBD (PM) +* Time: 1:00 - 5:00 PM Pacific Time ## **Abstract** @@ -40,21 +40,21 @@ ## **Outline** -| **Title** | **Speaker** | **Duration** | Link | +| **Title** | **Speaker** | **Time (Duration)** | Link | |-----------|-------------|--------------|------| -| **Introduction** | Organizers | 10 minutes | | -| **Invited Talk:** COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) | 20 minutes | | -| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-university) | 20 minutes | | -| Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation | Dmitri Goldenberg, Javier Albert (Booking.com) | 15 minutes | | -| Ensemble Method for Estimating Individualized Treatment Effects | Kevin Wu Han, Han Wu (Stanford) | 15 minutes | | -| A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 15 minutes | | -| An IPW-based Unbiased Ranking Metric in Two-sided Markets | Keisho Oh, Naoki Nishimura (Recruit Co), Minje Sung, Ken Kobayashi, Kazuhide Nakata (Tokyo Institute of Technology) | 15 minutes | | -| **Break & Poster Session** | | 30 minutes | | -| **Invited Talk:** Unit Selection Based on Counterfactual Logic | [Ang Li](#ang-li-university-of-california-los-angeles) | 20 minutes | | -| **Invited Talk:** Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 20 minutes | | -| Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments | Ali O Polat (Shipt) | 15 minutes | | -| Dynamic Causal Structure Discovery and Causal Effect Estimation | Jianian Wang, Rui Song (NCSU) | 15 minutes | | -| Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference | Yufei Wu, Zhiying Gu, Alex Deng, Jacob Zhu (Airbnb) | 15 minutes | | +| **Introduction** | Organizers | 1:00 - 1:10 PM (10 minutes) | | +| **Invited Talk:** COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) | 1:10 - 1:30 PM (20 minutes) | | +| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-university) | 1:30 - 1:50 PM (20 minutes) | | +| Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation | Dmitri Goldenberg, Javier Albert (Booking.com) | 1:50 - 2:05 PM (15 minutes) | | +| Ensemble Method for Estimating Individualized Treatment Effects | Kevin Wu Han, Han Wu (Stanford) | 2:05 - 2:20 PM (15 minutes) | | +| A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 2:20 - 2:35 PM (15 minutes) | | +| An IPW-based Unbiased Ranking Metric in Two-sided Markets | Keisho Oh, Naoki Nishimura (Recruit Co), Minje Sung, Ken Kobayashi, Kazuhide Nakata (Tokyo Institute of Technology) | 2:35 - 2:50 PM (15 minutes) | | +| **Break & Poster Session** | | 3:00 - 3:30 PM (30 minutes) | | +| **Invited Talk:** Unit Selection Based on Counterfactual Logic | [Ang Li](#ang-li-university-of-california-los-angeles) | 3:30 - 3:50 PM (20 minutes) | | +| **Invited Talk:** Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 3:50 - 4:10 PM (20 minutes) | | +| Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments | Ali O Polat (Shipt) | 4:10 - 4:25 PM (15 minutes) | | +| Dynamic Causal Structure Discovery and Causal Effect Estimation | Jianian Wang, Rui Song (NCSU) | 4:25 - 4:40 PM (15 minutes) | | +| Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference | Yufei Wu, Zhiying Gu, Alex Deng, Jacob Zhu (Airbnb) | 4:40 - 4:55 PM (15 minutes) | | ## **Invited Speakers** From bc4de829f4e03798f0a939779ec07c331082a1fa Mon Sep 17 00:00:00 2001 From: Jeong-Yoon Lee Date: Fri, 4 Aug 2023 10:15:04 -0700 Subject: [PATCH 32/33] add speakers' affiliation to the schedule --- README.md | 8 ++++---- index.html | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 995b304..2202ce7 100755 --- a/README.md +++ b/README.md @@ -49,15 +49,15 @@ submission guidelines. | **Title** | **Speaker** | **Time (Duration)** | Link | |-----------|-------------|--------------|------| | **Introduction** | Organizers | 1:00 - 1:10 PM (10 minutes) | | -| **Invited Talk:** COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) | 1:10 - 1:30 PM (20 minutes) | | -| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-university) | 1:30 - 1:50 PM (20 minutes) | | +| **Invited Talk:** COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) (Amazon) | 1:10 - 1:30 PM (20 minutes) | | +| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-university) (Emory) | 1:30 - 1:50 PM (20 minutes) | | | Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation | Dmitri Goldenberg, Javier Albert (Booking.com) | 1:50 - 2:05 PM (15 minutes) | | | Ensemble Method for Estimating Individualized Treatment Effects | Kevin Wu Han, Han Wu (Stanford) | 2:05 - 2:20 PM (15 minutes) | | | A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 2:20 - 2:35 PM (15 minutes) | | | An IPW-based Unbiased Ranking Metric in Two-sided Markets | Keisho Oh, Naoki Nishimura (Recruit Co), Minje Sung, Ken Kobayashi, Kazuhide Nakata (Tokyo Institute of Technology) | 2:35 - 2:50 PM (15 minutes) | | | **Break & Poster Session** | | 3:00 - 3:30 PM (30 minutes) | | -| **Invited Talk:** Unit Selection Based on Counterfactual Logic | [Ang Li](#ang-li-university-of-california-los-angeles) | 3:30 - 3:50 PM (20 minutes) | | -| **Invited Talk:** Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 3:50 - 4:10 PM (20 minutes) | | +| **Invited Talk:** Unit Selection Based on Counterfactual Logic | [Ang Li](#ang-li-university-of-california-los-angeles) (UCLA) | 3:30 - 3:50 PM (20 minutes) | | +| **Invited Talk:** Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) (Stanford/EconML) | 3:50 - 4:10 PM (20 minutes) | | | Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments | Ali O Polat (Shipt) | 4:10 - 4:25 PM (15 minutes) | | | Dynamic Causal Structure Discovery and Causal Effect Estimation | Jianian Wang, Rui Song (NCSU) | 4:25 - 4:40 PM (15 minutes) | | | Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference | Yufei Wu, Zhiying Gu, Alex Deng, Jacob Zhu (Airbnb) | 4:40 - 4:55 PM (15 minutes) | | diff --git a/index.html b/index.html index a54eb91..14bd462 100644 --- a/index.html +++ b/index.html @@ -43,15 +43,15 @@ | **Title** | **Speaker** | **Time (Duration)** | Link | |-----------|-------------|--------------|------| | **Introduction** | Organizers | 1:00 - 1:10 PM (10 minutes) | | -| **Invited Talk:** COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) | 1:10 - 1:30 PM (20 minutes) | | -| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-university) | 1:30 - 1:50 PM (20 minutes) | | +| **Invited Talk:** COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) (Amazon) | 1:10 - 1:30 PM (20 minutes) | | +| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-university) (Emory) | 1:30 - 1:50 PM (20 minutes) | | | Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation | Dmitri Goldenberg, Javier Albert (Booking.com) | 1:50 - 2:05 PM (15 minutes) | | | Ensemble Method for Estimating Individualized Treatment Effects | Kevin Wu Han, Han Wu (Stanford) | 2:05 - 2:20 PM (15 minutes) | | | A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 2:20 - 2:35 PM (15 minutes) | | | An IPW-based Unbiased Ranking Metric in Two-sided Markets | Keisho Oh, Naoki Nishimura (Recruit Co), Minje Sung, Ken Kobayashi, Kazuhide Nakata (Tokyo Institute of Technology) | 2:35 - 2:50 PM (15 minutes) | | | **Break & Poster Session** | | 3:00 - 3:30 PM (30 minutes) | | -| **Invited Talk:** Unit Selection Based on Counterfactual Logic | [Ang Li](#ang-li-university-of-california-los-angeles) | 3:30 - 3:50 PM (20 minutes) | | -| **Invited Talk:** Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) | 3:50 - 4:10 PM (20 minutes) | | +| **Invited Talk:** Unit Selection Based on Counterfactual Logic | [Ang Li](#ang-li-university-of-california-los-angeles) (UCLA) | 3:30 - 3:50 PM (20 minutes) | | +| **Invited Talk:** Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) (Stanford/EconML) | 3:50 - 4:10 PM (20 minutes) | | | Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments | Ali O Polat (Shipt) | 4:10 - 4:25 PM (15 minutes) | | | Dynamic Causal Structure Discovery and Causal Effect Estimation | Jianian Wang, Rui Song (NCSU) | 4:25 - 4:40 PM (15 minutes) | | | Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference | Yufei Wu, Zhiying Gu, Alex Deng, Jacob Zhu (Airbnb) | 4:40 - 4:55 PM (15 minutes) | | From ab4b212743e87e501f8611e03df5586477af6010 Mon Sep 17 00:00:00 2001 From: jpansnap <99832397+jpansnap@users.noreply.github.com> Date: Tue, 22 Aug 2023 21:42:25 -0700 Subject: [PATCH 33/33] Update README.md update slides and paper links --- README.md | 47 +++++++++++++++++++++++------------------------ 1 file changed, 23 insertions(+), 24 deletions(-) diff --git a/README.md b/README.md index 2202ce7..f8a9359 100755 --- a/README.md +++ b/README.md @@ -49,18 +49,18 @@ submission guidelines. | **Title** | **Speaker** | **Time (Duration)** | Link | |-----------|-------------|--------------|------| | **Introduction** | Organizers | 1:00 - 1:10 PM (10 minutes) | | -| **Invited Talk:** COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) (Amazon) | 1:10 - 1:30 PM (20 minutes) | | -| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-university) (Emory) | 1:30 - 1:50 PM (20 minutes) | | -| Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation | Dmitri Goldenberg, Javier Albert (Booking.com) | 1:50 - 2:05 PM (15 minutes) | | -| Ensemble Method for Estimating Individualized Treatment Effects | Kevin Wu Han, Han Wu (Stanford) | 2:05 - 2:20 PM (15 minutes) | | -| A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 2:20 - 2:35 PM (15 minutes) | | -| An IPW-based Unbiased Ranking Metric in Two-sided Markets | Keisho Oh, Naoki Nishimura (Recruit Co), Minje Sung, Ken Kobayashi, Kazuhide Nakata (Tokyo Institute of Technology) | 2:35 - 2:50 PM (15 minutes) | | +| **Invited Talk:** COG: Creative Optimality Gap for Video Advertising | [Raif Rustamov](#raif-rustamov-amazon) (Amazon) | 1:10 - 1:30 PM (20 minutes) | [Slides](https://drive.google.com/file/d/1ehHzNj-EDlhpQzCOhlJ9oE7Lnmf2Fpmc/view?usp=sharing)| +| **Invited Talk:** The Value of Last-Mile Delivery in Online Retail | [Ruomeng Cui](#ruomeng-cui-emory-university) (Emory) | 1:30 - 1:50 PM (20 minutes) | [Slides](https://drive.google.com/file/d/19w3ay80K1xBqceZj6DgBtyJgPx2OrYe6/view?usp=drive_link)| +| Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation | Dmitri Goldenberg, Javier Albert (Booking.com) | 1:50 - 2:05 PM (15 minutes) | [Slides](https://docs.google.com/presentation/d/1Fz720lBj8DDsviLYNAIyBLVE7TN8qFoVtxPzbfAvo_I/edit?usp=drive_link)| +| Ensemble Method for Estimating Individualized Treatment Effects | Kevin Wu Han, Han Wu (Stanford) | 2:05 - 2:20 PM (15 minutes) | [Slides](https://drive.google.com/file/d/1KLAFpKYw5mlJQ7cNyLamS1LkuGScABSb/view?usp=sharing), [Paper](https://drive.google.com/file/d/1dzsIGgQ4cF2ltetH16A4Zz-L-pZ6tHBK/view?usp=drive_link) | +| A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization | Nicolò Cosimo Albanese, Fabian Furrer, Marco Guerriero (Amazon AWS) | 2:20 - 2:35 PM (15 minutes) | [Slides](https://drive.google.com/file/d/1uSqpV51qxNcEyCrgzhYo2nr44tfn717z/view?usp=drive_link), [Paper](https://drive.google.com/file/d/1VlW-zgrCfaKi5CtGYkQbkhWw7JVXhPtw/view?usp=drive_link) | +| An IPW-based Unbiased Ranking Metric in Two-sided Markets | Keisho Oh, Naoki Nishimura (Recruit Co), Minje Sung, Ken Kobayashi, Kazuhide Nakata (Tokyo Institute of Technology) | 2:35 - 2:50 PM (15 minutes) | [Slides](https://drive.google.com/file/d/1XLQCMUNy79jmYb0y1AB7ImRflq4csh6Y/view?usp=drive_link), [Paper](https://drive.google.com/file/d/1Bshr6dwFB-E2H2K64g9OfERNUQcfP0eb/view?usp=drive_link)| | **Break & Poster Session** | | 3:00 - 3:30 PM (30 minutes) | | -| **Invited Talk:** Unit Selection Based on Counterfactual Logic | [Ang Li](#ang-li-university-of-california-los-angeles) (UCLA) | 3:30 - 3:50 PM (20 minutes) | | -| **Invited Talk:** Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) (Stanford/EconML) | 3:50 - 4:10 PM (20 minutes) | | -| Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments | Ali O Polat (Shipt) | 4:10 - 4:25 PM (15 minutes) | | -| Dynamic Causal Structure Discovery and Causal Effect Estimation | Jianian Wang, Rui Song (NCSU) | 4:25 - 4:40 PM (15 minutes) | | -| Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference | Yufei Wu, Zhiying Gu, Alex Deng, Jacob Zhu (Airbnb) | 4:40 - 4:55 PM (15 minutes) | | +| **Invited Talk:** Unit Selection Based on Counterfactual Logic | [Ang Li](#ang-li-university-of-california-los-angeles) (UCLA) | 3:30 - 3:50 PM (20 minutes) | [Slides](https://drive.google.com/file/d/1P-it4MNrYbnWNUgodagU69oubVtpo_WV/view?usp=drive_link)| +| **Invited Talk:** Towards Automating the Causal Machine Learning Pipeline | [Vasilis Syrgkanis](#vasilis-syrgkanis-stanford-universityeconml) (Stanford/EconML) | 3:50 - 4:10 PM (20 minutes) | [Slides](https://www.dropbox.com/scl/fi/w2p1cnghhqp1qc377o4yu/auto_debiased.pptx?rlkey=8k4bdcrastqmzlao8n5bng258&dl=0)| +| Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments | Ali O Polat (Shipt) | 4:10 - 4:25 PM (15 minutes) | [Slides](https://drive.google.com/file/d/1F9bCmoCYg7AeNxKXekCze7a4ELXs2Ohv/view?usp=drive_link), [Paper](https://drive.google.com/file/d/1rDsCmwl23HELiD-P_Qq9TUZZ_3opbEji/view?usp=drive_link)| +| Dynamic Causal Structure Discovery and Causal Effect Estimation | Jianian Wang, Rui Song (NCSU) | 4:25 - 4:40 PM (15 minutes) | [Slides](https://drive.google.com/file/d/13MzjyZLGxfGNoAg3TMRF0Es-TqtnRVSH/view?usp=drive_link), [Paper](https://drive.google.com/file/d/1928QcX3PdFan_gkeJl-mxEgl7kQ9KupN/view?usp=drive_link)| +| Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference | Yufei Wu, Zhiying Gu, Alex Deng, Jacob Zhu (Airbnb) | 4:40 - 4:55 PM (15 minutes) | [Slides](https://drive.google.com/file/d/1r0xXFFflSVDFNtGEypUGh845CeycF51q/view?usp=drive_link), [Paper](https://drive.google.com/file/d/1HQekrFF1vNNrO7TF4aX43850-Dc6UHgo/view?usp=drive_link)| ## **Invited Speakers** @@ -153,21 +153,20 @@ received his Ph.D. in Computer Science from Cornell University. ## **Accepted Papers** ### For Oral Presentation -1. Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation, Dmitri Goldenberg (Booking.com)*; Javier Albert (Booking.com) -2. Ensemble Method for Estimating Individualized Treatment Effects, Kevin Wu Han (Stanford University)*; Han Wu (Stanford University) -3. A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization, Nicolò Cosimo Albanese (AWS)*; Fabian Furrer (AWS); Marco Guerriero (AWS) -4. An IPW-based Unbiased Ranking Metric in Two-sided Markets, Keisho Oh (Recruit Co., Ltd.)*; Naoki Nishimura (Recruit Co., Ltd.); Minje Sung (Tokyo Institute of Technology); Ken Kobayashi (Tokyo Institute of Technology); Kazuhide Nakata (Department of Industrial Engineering and Economics, Tokyo Institute of Technology.) -5. Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments, Ali O Polat (Shipt Inc.)* -6. Dynamic Causal Structure Discovery and Causal Effect Estimation, Jianian Wang (North Carolina State Unicersity)*; Rui Song (North Carolina State Unicersity) -7. Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference, Yufei Wu (Airbnb)*; Zhiying Gu (Airbnb); Alex Deng (Airbnb); Jacob Zhu (Airbnb) +1. Leveraging Causal Uplift Modeling for Budget Constrained Benefits Allocation, Dmitri Goldenberg (Booking.com)*; Javier Albert (Booking.com); [Slides](https://docs.google.com/presentation/d/1Fz720lBj8DDsviLYNAIyBLVE7TN8qFoVtxPzbfAvo_I/edit?usp=drive_link) +2. Ensemble Method for Estimating Individualized Treatment Effects, Kevin Wu Han (Stanford University)*; Han Wu (Stanford University); [Slides](https://drive.google.com/file/d/1KLAFpKYw5mlJQ7cNyLamS1LkuGScABSb/view?usp=sharing), [Paper](https://drive.google.com/file/d/1dzsIGgQ4cF2ltetH16A4Zz-L-pZ6tHBK/view?usp=drive_link) +3. A Scalable and Debiased Approach to Dynamic Pricing with Causal Machine Learning and Optimization, Nicolò Cosimo Albanese (AWS)*; Fabian Furrer (AWS); Marco Guerriero (AWS); [Slides](https://drive.google.com/file/d/1uSqpV51qxNcEyCrgzhYo2nr44tfn717z/view?usp=drive_link), [Paper](https://drive.google.com/file/d/1VlW-zgrCfaKi5CtGYkQbkhWw7JVXhPtw/view?usp=drive_link) +4. An IPW-based Unbiased Ranking Metric in Two-sided Markets, Keisho Oh (Recruit Co., Ltd.)*; Naoki Nishimura (Recruit Co., Ltd.); Minje Sung (Tokyo Institute of Technology); Ken Kobayashi (Tokyo Institute of Technology); Kazuhide Nakata (Department of Industrial Engineering and Economics, Tokyo Institute of Technology.); [Slides](https://drive.google.com/file/d/1XLQCMUNy79jmYb0y1AB7ImRflq4csh6Y/view?usp=drive_link), [Paper](https://drive.google.com/file/d/1Bshr6dwFB-E2H2K64g9OfERNUQcfP0eb/view?usp=drive_link) +5. Power and Pre-treatment Fit: Optimizing Synthetic Control Method for Quasi-experiments, Ali O Polat (Shipt Inc.)*; [Slides](https://drive.google.com/file/d/1F9bCmoCYg7AeNxKXekCze7a4ELXs2Ohv/view?usp=drive_link), [Paper](https://drive.google.com/file/d/1rDsCmwl23HELiD-P_Qq9TUZZ_3opbEji/view?usp=drive_link) +6. Dynamic Causal Structure Discovery and Causal Effect Estimation, Jianian Wang (North Carolina State Unicersity)*; Rui Song (North Carolina State Unicersity); [Slides](https://drive.google.com/file/d/13MzjyZLGxfGNoAg3TMRF0Es-TqtnRVSH/view?usp=drive_link), [Paper](https://drive.google.com/file/d/1928QcX3PdFan_gkeJl-mxEgl7kQ9KupN/view?usp=drive_link) +7. Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference, Yufei Wu (Airbnb)*; Zhiying Gu (Airbnb); Alex Deng (Airbnb); Jacob Zhu (Airbnb); [Slides](https://drive.google.com/file/d/1r0xXFFflSVDFNtGEypUGh845CeycF51q/view?usp=drive_link), [Paper](https://drive.google.com/file/d/1HQekrFF1vNNrO7TF4aX43850-Dc6UHgo/view?usp=drive_link) ### For Poster Presentation -8. Community Detection-Enhanced Causal Structural Learning, Yuhe Gao (North Carolina State University)*; Hengrui Cai (University of California Irvine); Sheng Zhang (North Carolina State University); Rui Song (North Carolina State University) -9. ACE: Active Learning for Causal Inference with Expensive Experiments, Difan Song (Georgia Institute of Technology)*; Simon Mak (Duke University); C.F. Jeff Wu (Georgia Institute of Technology) -10. Extracting Causal Insights from Microsoft Feedback Hub using LLMs and In-context Learning, Sara Abdali (University of California, Riverside )*; Anjali Parikh (Microsoft); Steve Lim (Microsoft) -11. Evaluate the Impact of Similar Products Ad Group Recommendations with Causal Inference, Jamie Chen (Amazon)*; Zuqi Shang (AmaOn); Raif Rustamov (Amazon) -12. Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing, Shahin Boluki (Pros Inc)*; Ravi Kumar (PROS) -13. OpportunityFinder: A Framework for Automated Causal Inference, Huy Nguyen (Amazon)*; Prince Grover (Amazon); Devashish Khatwani (Amazon) +8. Community Detection-Enhanced Causal Structural Learning, Yuhe Gao (North Carolina State University)*; Hengrui Cai (University of California Irvine); Sheng Zhang (North Carolina State University); Rui Song (North Carolina State University); [Poster](https://drive.google.com/file/d/1dC_WLUhOJletn-kDJd1GPUJDyHwpMblG/view?usp=drive_link), [Paper](https://drive.google.com/file/d/11vnSFYDQZ1ZjztaM_eW7J9jd5tKH7y6Y/view?usp=drive_link) +9. ACE: Active Learning for Causal Inference with Expensive Experiments, Difan Song (Georgia Institute of Technology)*; Simon Mak (Duke University); C.F. Jeff Wu (Georgia Institute of Technology); [Paper](https://drive.google.com/file/d/1-fCLcT4RYAHJlsSBUuuAwsuqHDf9B4Qt/view?usp=drive_link) +10. Evaluate the Impact of Similar Products Ad Group Recommendations with Causal Inference, Jamie Chen (Amazon)*; Zuqi Shang (AmaOn); Raif Rustamov (Amazon); [Poster](https://drive.google.com/file/d/13UTLTuJKBA5hIFZ0g1HolLHQpAXhqcX4/view?usp=drive_link), [Paper](https://drive.google.com/file/d/1ahjN_fNvDjmdwD0btW4UvgxaKgCB32kP/view?usp=drive_link) +11. Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing, Shahin Boluki (Pros Inc)*; Ravi Kumar (PROS); [Poster](https://drive.google.com/file/d/1AcRtI5NtovYWMEOLnQ7dE_IRgyHVukqf/view?usp=drive_link), [Paper](https://drive.google.com/file/d/1n5mL1quGS5jWVZXXqUrL349CJH-I2BOW/view?usp=drive_link) +12. OpportunityFinder: A Framework for Automated Causal Inference, Huy Nguyen (Amazon)*; Prince Grover (Amazon); Devashish Khatwani (Amazon); [Poster](https://drive.google.com/file/d/1MH_vV5MDafqAOVLnQRIgmG1IFGXZx05r/view?usp=drive_link), [Paper](https://drive.google.com/file/d/1_yAoohM0jG0uPi7om9_6igVQJI1JM-Ce/view?usp=drive_link) ## **Organizers**

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