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68 changes: 68 additions & 0 deletions .github/workflows/auto-build.yml
Original file line number Diff line number Diff line change
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name: auto_build

# Controls when the workflow will run
on:
# Triggers the workflow on push or pull request events but only for the "main" branch
push:
branches: ["auto-build"]
# pull_request:
# branches: [ "main" ]

# Allows you to run this workflow manually from the Actions tab
workflow_dispatch:

# A workflow run is made up of one or more jobs that can run sequentially or in parallel



jobs:
BuildJB:
runs-on: ubuntu-22.04
env:
GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
R_KEEP_PKG_SOURCE: yes
steps:

# Install dependencies Pyhton
- uses: actions/checkout@v2
- id: Python_pip
name: Python dependencies
uses: actions/setup-python@v2
with:
python-version: '3.8'
cache: 'pip'
- run: pip install git+https://github.com/gsbDBI/ds-wgan
- run: pip install -r requirements.txt

# Build book
- id: Build_book
name: Build
run: jb clean -a . ; jb build .
shell: bash

# Update book in gh-pages branch

- id: ghp
name: none
run: ghp-import -n -p -f _build/html
shell: bash

# Push in main

- id: commit
name: Commit files
run:
git config --local user.name action-user

git config --local user.email "actions@github.com"

git add -A

git commit -m "update files"

git push origin auto_build

env:
REPO_KEY: ${{secrets.GITHUB_TOKEN}}

username: github-actions
4 changes: 2 additions & 2 deletions _config.yml
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Expand Up @@ -3,7 +3,7 @@

title: MGTECON 634 at Stanford (Python scripts)
author: Phd Susan Athey
logo: figs/logo.jpg
logo: figs/logo.png
description: This tutorial will introduce key concepts in machine learning-based causal inference. This tutorial is used in the MGTECON 634 at Stanford. Scripts were translated into Python.

parse:
Expand Down Expand Up @@ -96,4 +96,4 @@ sphinx:
google_analytics:
mytrackingcode: UA-205698170-1



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20 changes: 10 additions & 10 deletions notebooks/6_policy_learning_1.ipynb

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29 changes: 29 additions & 0 deletions requirements.txt
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@@ -1,3 +1,32 @@
jupyter-book
matplotlib
numpy
pandas
seaborn
# random
# math
# warnings
sklearn
# SynRNG
# request
# io
statsmodels
# itertools
scipy
graphviz
simple_colors
patsy
# string
econml
# re
CTL
plotnine
# csv
# time
rpy2
imageio
# png
# PIL
pypng
# wgan
ghp-import