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Merge pull request #262 from DoubleML/dev
Update docs for Release 0.11.1
2 parents 91e260f + 87cb744 commit 3ab343b

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doc/api/utility.rst

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utils.DMLDummyRegressor
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utils.DMLDummyClassifier
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utils.DMLOptunaResult
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utils.DoubleMLBLP
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utils.DoubleMLPolicyTree
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utils.GlobalRegressor

doc/examples/index.rst

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py_double_ml_irm_vs_apo.ipynb
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py_double_ml_lplr.ipynb
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py_double_ml_ssm.ipynb
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py_double_ml_learner.ipynb
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learners/py_optuna.ipynb
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learners/py_learner.ipynb
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py_double_ml_firststage.ipynb
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py_double_ml_multiway_cluster.ipynb
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py_double_ml_sensitivity_booking.ipynb

doc/examples/py_double_ml_learner.ipynb renamed to doc/examples/learners/py_learner.ipynb

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"source": [
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"# Python: Choice of learners\n",
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"\n",
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"This notebooks contains some practical recommendations to choose the right learner and evaluate different learners for the corresponding nuisance components.\n",
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"This notebook contains some practical recommendations to choose the right learner and evaluate different learners for the corresponding nuisance components.\n",
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"This notebook mainly highlights the differences in using different learners, i.e. linear or tree-based methods. Generally, we recommend to tune hyperparameters for the chosen learners, see [Example Gallery](https://docs.doubleml.org/stable/examples/index.html).\n",
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"\n",
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"For the example, we will work with a IRM, but all of the important components are directly usable for all other models too.\n",
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"For the example, we will work with a IRM, but all of the important components are directly usable for all other models, too.\n",
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"\n",
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"To be able to compare the properties of different learners, we will start by setting the true treatment parameter to zero, fix some other parameters of the data generating process and generate several datasets \n",
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"to obtain some information about the distribution of the estimators."

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