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Signed-off-by: Keith Battocchi <kebatt@microsoft.com>
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LICENSE
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@ -1,6 +1,6 @@
MIT License
Copyright (c) Microsoft Corporation. All rights reserved.
Copyright (c) PyWhy contributors. All rights reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal

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README.md
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@ -46,63 +46,63 @@ For information on use cases and background material on causal inference and het
# News
**November 16, 2022:** Release v0.14.0, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.14.0)
**November 16, 2022:** Release v0.14.0, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.14.0)
<details><summary>Previous releases</summary>
**June 17, 2022:** Release v0.13.1, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.13.1)
**June 17, 2022:** Release v0.13.1, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.13.1)
**January 31, 2022:** Release v0.13.0, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.13.0)
**January 31, 2022:** Release v0.13.0, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.13.0)
**August 13, 2021:** Release v0.12.0, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.12.0)
**August 13, 2021:** Release v0.12.0, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.12.0)
**August 5, 2021:** Release v0.12.0b6, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.12.0b6)
**August 5, 2021:** Release v0.12.0b6, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.12.0b6)
**August 3, 2021:** Release v0.12.0b5, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.12.0b5)
**August 3, 2021:** Release v0.12.0b5, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.12.0b5)
**July 9, 2021:** Release v0.12.0b4, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.12.0b4)
**July 9, 2021:** Release v0.12.0b4, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.12.0b4)
**June 25, 2021:** Release v0.12.0b3, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.12.0b3)
**June 25, 2021:** Release v0.12.0b3, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.12.0b3)
**June 18, 2021:** Release v0.12.0b2, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.12.0b2)
**June 18, 2021:** Release v0.12.0b2, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.12.0b2)
**June 7, 2021:** Release v0.12.0b1, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.12.0b1)
**June 7, 2021:** Release v0.12.0b1, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.12.0b1)
**May 18, 2021:** Release v0.11.1, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.11.1)
**May 18, 2021:** Release v0.11.1, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.11.1)
**May 8, 2021:** Release v0.11.0, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.11.0)
**May 8, 2021:** Release v0.11.0, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.11.0)
**March 22, 2021:** Release v0.10.0, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.10.0)
**March 22, 2021:** Release v0.10.0, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.10.0)
**March 11, 2021:** Release v0.9.2, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.9.2)
**March 11, 2021:** Release v0.9.2, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.9.2)
**March 3, 2021:** Release v0.9.1, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.9.1)
**March 3, 2021:** Release v0.9.1, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.9.1)
**February 20, 2021:** Release v0.9.0, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.9.0)
**February 20, 2021:** Release v0.9.0, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.9.0)
**January 20, 2021:** Release v0.9.0b1, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.9.0b1)
**January 20, 2021:** Release v0.9.0b1, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.9.0b1)
**November 20, 2020:** Release v0.8.1, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.8.1)
**November 20, 2020:** Release v0.8.1, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.8.1)
**November 18, 2020:** Release v0.8.0, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.8.0)
**November 18, 2020:** Release v0.8.0, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.8.0)
**September 4, 2020:** Release v0.8.0b1, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.8.0b1)
**September 4, 2020:** Release v0.8.0b1, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.8.0b1)
**March 6, 2020:** Release v0.7.0, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.7.0)
**March 6, 2020:** Release v0.7.0, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.7.0)
**February 18, 2020:** Release v0.7.0b1, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.7.0b1)
**February 18, 2020:** Release v0.7.0b1, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.7.0b1)
**January 10, 2020:** Release v0.6.1, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.6.1)
**January 10, 2020:** Release v0.6.1, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.6.1)
**December 6, 2019:** Release v0.6, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.6)
**December 6, 2019:** Release v0.6, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.6)
**November 21, 2019:** Release v0.5, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.5).
**November 21, 2019:** Release v0.5, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.5).
**June 3, 2019:** Release v0.4, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.4).
**June 3, 2019:** Release v0.4, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.4).
**May 3, 2019:** Release v0.3, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.3).
**May 3, 2019:** Release v0.3, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.3).
**April 10, 2019:** Release v0.2, see release notes [here](https://github.com/Microsoft/EconML/releases/tag/v0.2).
**April 10, 2019:** Release v0.2, see release notes [here](https://github.com/py-why/EconML/releases/tag/v0.2).
**March 6, 2019:** Release v0.1, welcome to have a try and provide feedback.
@ -645,7 +645,7 @@ importances = policy.feature_importances_
![image](images/policy_tree.png)
</details>
To see more complex examples, go to the [notebooks](https://github.com/Microsoft/EconML/tree/main/notebooks) section of the repository. For a more detailed description of the treatment effect estimation algorithms, see the EconML [documentation](https://econml.azurewebsites.net/).
To see more complex examples, go to the [notebooks](https://github.com/py-why/EconML/tree/main/notebooks) section of the repository. For a more detailed description of the treatment effect estimation algorithms, see the EconML [documentation](https://econml.azurewebsites.net/).
# For Developers
@ -667,7 +667,7 @@ This project's documentation is generated via [Sphinx](https://www.sphinx-doc.or
To generate a local copy of the documentation from a clone of this repository, just run `python setup.py build_sphinx -W -E -a`, which will build the documentation and place it under the `build/sphinx/html` path.
The reStructuredText files that make up the documentation are stored in the [docs directory](https://github.com/Microsoft/EconML/tree/main/doc); module documentation is automatically generated by the Sphinx build process.
The reStructuredText files that make up the documentation are stored in the [docs directory](https://github.com/py-why/EconML/tree/main/doc); module documentation is automatically generated by the Sphinx build process.
## Release process
@ -692,7 +692,7 @@ We use GitHub Actions to build and publish the package and documentation. To cr
If you use EconML in your research, please cite us as follows:
Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Paul Oka, Miruna Oprescu, Vasilis Syrgkanis. **EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation.** https://github.com/microsoft/EconML, 2019. Version 0.x.
Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Paul Oka, Miruna Oprescu, Vasilis Syrgkanis. **EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation.** https://github.com/py-why/EconML, 2019. Version 0.x.
BibTex:
@ -700,7 +700,7 @@ BibTex:
@misc{econml,
author={Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Paul Oka, Miruna Oprescu, Vasilis Syrgkanis},
title={{EconML}: {A Python Package for ML-Based Heterogeneous Treatment Effects Estimation}},
howpublished={https://github.com/microsoft/EconML},
howpublished={https://github.com/py-why/EconML},
note={Version 0.x},
year={2019}
}

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doc/conf.py
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@ -21,8 +21,8 @@ sys.path.insert(0, os.path.abspath('econml'))
# -- Project information -----------------------------------------------------
project = 'econml'
copyright = '2022, Microsoft Research'
author = 'Microsoft Research'
copyright = '2022, PyWhy contributors'
author = 'PyWhy contributors'
version = econml.__version__
release = econml.__version__
@ -176,7 +176,7 @@ latex_elements = {
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(root_doc, 'econml.tex', 'econml Documentation',
'Microsoft Research', 'manual'),
'PyWhy contributors', 'manual'),
]

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doc/spec/estimation/dml.rst
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@ -607,8 +607,8 @@ Usage Examples
==================================
For more extensive examples check out the following notebooks:
`DML Examples Jupyter Notebook <https://github.com/microsoft/EconML/blob/main/notebooks/Double%20Machine%20Learning%20Examples.ipynb>`_,
`Forest Learners Jupyter Notebook <https://github.com/microsoft/EconML/blob/main/notebooks/ForestLearners%20Basic%20Example.ipynb>`_.
`DML Examples Jupyter Notebook <https://github.com/py-why/EconML/blob/main/notebooks/Double%20Machine%20Learning%20Examples.ipynb>`_,
`Forest Learners Jupyter Notebook <https://github.com/py-why/EconML/blob/main/notebooks/ForestLearners%20Basic%20Example.ipynb>`_.
.. rubric:: Single Outcome, Single Treatment

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doc/spec/estimation/dr.rst
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@ -366,7 +366,7 @@ Usage FAQs
If you care more about mean squared error than confidence intervals and hypothesis testing, then use the
:class:`.DRLearner` class and choose a cross-validated final model (checkout the
`Forest Learners Jupyter notebook <https://github.com/microsoft/EconML/blob/main/notebooks/ForestLearners%20Basic%20Example.ipynb>`_
`Forest Learners Jupyter notebook <https://github.com/py-why/EconML/blob/main/notebooks/ForestLearners%20Basic%20Example.ipynb>`_
for such an example).
Also the check out the :ref:`Orthogonal Random Forest User Guide <orthoforestuserguide>` or the
:ref:`Meta Learners User Guide <metalearnersuserguide>`.
@ -516,7 +516,7 @@ Usage Examples
Check out the following Jupyter notebooks:
* `Meta Learners Jupyter Notebook <https://github.com/microsoft/EconML/blob/main/notebooks/Metalearners%20Examples.ipynb>`_
* `Forest Learners Jupyter Notebook <https://github.com/microsoft/EconML/blob/main/notebooks/ForestLearners%20Basic%20Example.ipynb>`_
* `Meta Learners Jupyter Notebook <https://github.com/py-why/EconML/blob/main/notebooks/Metalearners%20Examples.ipynb>`_
* `Forest Learners Jupyter Notebook <https://github.com/py-why/EconML/blob/main/notebooks/ForestLearners%20Basic%20Example.ipynb>`_

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doc/spec/estimation/forest.rst
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@ -315,8 +315,8 @@ Usage Examples
Here is a simple example of how to call :class:`.DMLOrthoForest`
and what the returned values correspond to in a simple data generating process.
For more examples check out our
`OrthoForest Jupyter notebook <https://github.com/Microsoft/EconML/blob/main/notebooks/Orthogonal%20Random%20Forest%20Examples.ipynb>`_
and the `ForestLearners Jupyter notebook <https://github.com/microsoft/EconML/blob/main/notebooks/ForestLearners%20Basic%20Example.ipynb>`_ .
`OrthoForest Jupyter notebook <https://github.com/py-why/EconML/blob/main/notebooks/Orthogonal%20Random%20Forest%20Examples.ipynb>`_
and the `ForestLearners Jupyter notebook <https://github.com/py-why/EconML/blob/main/notebooks/ForestLearners%20Basic%20Example.ipynb>`_ .
.. testcode::

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doc/spec/estimation/metalearners.rst
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@ -17,7 +17,7 @@ These methods fall into the meta-learner category because they simply combine ML
so as to get a final stage estimate and do not introduce new estimation components.
For examples of how to use our implemented metelearners check out this
`Metalearners Jupyter notebook <https://github.com/Microsoft/EconML/blob/main/notebooks/Metalearners%20Examples.ipynb>`_. The examples
`Metalearners Jupyter notebook <https://github.com/py-why/EconML/blob/main/notebooks/Metalearners%20Examples.ipynb>`_. The examples
and documents here are only based on binary treatment setting, but all of these estimators are applicable to multiple treatment settings as well.
@ -146,9 +146,9 @@ Usage Examples
Check out the following notebooks:
* `Metalearners Jupyter notebook <https://github.com/Microsoft/EconML/blob/main/notebooks/Metalearners%20Examples.ipynb>`_.
* `DML Examples Jupyter Notebook <https://github.com/microsoft/EconML/blob/main/notebooks/Double%20Machine%20Learning%20Examples.ipynb>`_,
* `Forest Learners Jupyter Notebook <https://github.com/microsoft/EconML/blob/main/notebooks/ForestLearners%20Basic%20Example.ipynb>`_.
* `Metalearners Jupyter notebook <https://github.com/py-why/EconML/blob/main/notebooks/Metalearners%20Examples.ipynb>`_.
* `DML Examples Jupyter Notebook <https://github.com/py-why/EconML/blob/main/notebooks/Double%20Machine%20Learning%20Examples.ipynb>`_,
* `Forest Learners Jupyter Notebook <https://github.com/py-why/EconML/blob/main/notebooks/ForestLearners%20Basic%20Example.ipynb>`_.
.. todo::

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doc/spec/estimation/orthoiv.rst
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@ -77,4 +77,4 @@ Usage Examples
==================================
For more extensive examples check out the following notebooks:
`OrthoIV and DRIV Examples Jupyter Notebook <https://github.com/microsoft/EconML/blob/main/notebooks/OrthoIV%20and%20DRIV%20Examples.ipynb>`_.
`OrthoIV and DRIV Examples Jupyter Notebook <https://github.com/py-why/EconML/blob/main/notebooks/OrthoIV%20and%20DRIV%20Examples.ipynb>`_.

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doc/spec/motivation.rst
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@ -55,7 +55,7 @@ The DRIV model adjusts for the fact that not every customer who was offered the
became a member and returns the effect of membership rather than the effect of receiving the quick sign-up.
Link to jupyter notebook:
`Recommendation A/B Testing <https://github.com/microsoft/EconML/blob/main/notebooks/CustomerScenarios/Case%20Study%20-%20Recommendation%20AB%20Testing%20at%20An%20Online%20Travel%20Company.ipynb>`__
`Recommendation A/B Testing <https://github.com/py-why/EconML/blob/main/notebooks/CustomerScenarios/Case%20Study%20-%20Recommendation%20AB%20Testing%20at%20An%20Online%20Travel%20Company.ipynb>`__
More details:
`Trip Advisor Case Study <https://www.microsoft.com/en-us/research/uploads/prod/2020/04/MSR_ALICE_casestudy_2020.pdf>`__
@ -82,7 +82,7 @@ The tree interpreter provides a presentation-ready summary of the key features
that explain the biggest differences in responsiveness to a discount.
Link to jupyter notebook:
`Customer Segmentation <https://github.com/microsoft/EconML/blob/main/notebooks/CustomerScenarios/Case%20Study%20-%20Customer%20Segmentation%20at%20An%20Online%20Media%20Company.ipynb>`__.
`Customer Segmentation <https://github.com/py-why/EconML/blob/main/notebooks/CustomerScenarios/Case%20Study%20-%20Customer%20Segmentation%20at%20An%20Online%20Media%20Company.ipynb>`__.
Multi-investment Attribution
-----------------------------
@ -103,4 +103,4 @@ The model uses flexible functions of observed customer features to filter out co
in existing data and deliver the causal effect of each effort on revenue.
Link to jupyter notebook:
`Multi-investment Attribution <https://github.com/microsoft/EconML/blob/main/notebooks/CustomerScenarios/Case%20Study%20-%20Multi-investment%20Attribution%20at%20A%20Software%20Company.ipynb>`__.
`Multi-investment Attribution <https://github.com/py-why/EconML/blob/main/notebooks/CustomerScenarios/Case%20Study%20-%20Multi-investment%20Attribution%20at%20A%20Software%20Company.ipynb>`__.

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econml/__init__.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
__all__ = ['automated_ml',

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econml/_cate_estimator.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Base classes for all CATE estimators."""

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econml/_ensemble/__init__.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from ._ensemble import BaseEnsemble, _partition_estimators

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econml/_ensemble/_ensemble.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
#
# This code is a fork from:

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econml/_ensemble/_utilities.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import numbers

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econml/_ortho_learner.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""

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econml/_shap.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Helper functions to get shap values for different cate estimators.

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econml/_tree_exporter.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
#
# This code contains some snippets of code from:

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econml/_version.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
__version__ = '0.14.0'

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econml/automated_ml/__init__.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from ._automated_ml import (setAutomatedMLWorkspace, addAutomatedML,

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econml/automated_ml/_automated_ml.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
# AzureML

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econml/cate_interpreter/__init__.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from ._interpreters import SingleTreeCateInterpreter, SingleTreePolicyInterpreter

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econml/cate_interpreter/_interpreters.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import abc

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econml/dml/__init__.py
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@ -1,5 +1,5 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Double Machine Learning. The method uses machine learning methods to identify the

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econml/dml/_rlearner.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""

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econml/dml/causal_forest.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from warnings import warn

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econml/dml/dml.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from warnings import warn

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econml/dowhy.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
@ -6,7 +6,7 @@
References
----------
DoWhy, https://microsoft.github.io/dowhy/
DoWhy, https://www.pywhy.org/dowhy/
"""

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econml/dr/__init__.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from ._drlearner import (DRLearner, LinearDRLearner, SparseLinearDRLearner, ForestDRLearner)

2
econml/dr/_drlearner.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""

2
econml/dynamic/__init__.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
__all__ = ["dml"]

2
econml/dynamic/dml/__init__.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Double Machine Learning for Dynamic Treatment Effects.

2
econml/grf/__init__.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
""" An efficient Cython implementation of Generalized Random Forests [grf]_ and special

2
econml/grf/_base_grf.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
#
# This code contains snippets of code from

2
econml/grf/_base_grftree.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
#
# This code contains snippets of code from:

2
econml/grf/_criterion.pxd
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
# published under the following license and copyright:
# BSD 3-Clause License

2
econml/grf/_criterion.pyx
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# cython: boundscheck=False
# cython: wraparound=False
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
#
# This code contains some snippets of code from:

2
econml/grf/_utils.pxd
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import numpy as np

2
econml/grf/_utils.pyx
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@ -2,7 +2,7 @@
# cython: boundscheck=False
# cython: wraparound=False
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from libc.stdlib cimport free

2
econml/grf/classes.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import numpy as np

2
econml/inference/__init__.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from ._inference import (BootstrapInference, GenericModelFinalInference, GenericSingleTreatmentModelFinalInference,

2
econml/inference/_bootstrap.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Bootstrap sampling."""

2
econml/inference/_inference.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import abc

2
econml/iv/__init__.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
__all__ = ["dml", "dr", "nnet", "sieve"]

2
econml/iv/dml/__init__.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Orthogonal IV for Heterogeneous Treatment Effects.

2
econml/iv/dml/_dml.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Orthogonal IV for Heterogeneous Treatment Effects.

2
econml/iv/dr/__init__.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Orthogonal IV for Heterogeneous Treatment Effects.

2
econml/iv/dr/_dr.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Doubly Robust IV for Heterogeneous Treatment Effects.

2
econml/iv/nnet/__init__.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from ._deepiv import DeepIV

2
econml/iv/nnet/_deepiv.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Deep IV estimator and related components."""

2
econml/iv/sieve/__init__.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from ._tsls import HermiteFeatures, DPolynomialFeatures, SieveTSLS

2
econml/iv/sieve/_tsls.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Provides a non-parametric two-stage least squares instrumental variable estimator."""

2
econml/metalearners/__init__.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from ._metalearners import (TLearner, SLearner, XLearner, DomainAdaptationLearner)

2
econml/metalearners/_metalearners.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Metalearners for heterogeneous treatment effects in the context of discrete treatments.

2
econml/orf/__init__.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
""" An implementation of Orthogonal Random Forests [orf]_ and special

2
econml/orf/_causal_tree.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Basic tree utilities and methods.

2
econml/orf/_ortho_forest.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Orthogonal Random Forest.

2
econml/panel/__init__.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
__all__ = ["dml"]

2
econml/panel/dml/__init__.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Double Machine Learning for Dynamic Treatment Effects.

2
econml/panel/dml/_dml.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import abc

2
econml/policy/__init__.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from ._forest import PolicyTree, PolicyForest

2
econml/policy/_base.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Base classes for all Policy estimators."""

2
econml/policy/_drlearner.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from warnings import warn

2
econml/policy/_forest/__init__.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from ._tree import PolicyTree

2
econml/policy/_forest/_criterion.pxd
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
# published under the following license and copyright:
# BSD 3-Clause License

2
econml/policy/_forest/_criterion.pyx
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@ -2,7 +2,7 @@
# cython: boundscheck=False
# cython: wraparound=False
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import numpy as np

2
econml/policy/_forest/_forest.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
#
# This code contains snippets of code from

2
econml/policy/_forest/_tree.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
#
# This code contains snippets of code from:

2
econml/score/__init__.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""

2
econml/score/ensemble_cate.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import numpy as np

2
econml/score/rscorer.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from ..dml import LinearDML

2
econml/sklearn_extensions/linear_model.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Collection of scikit-learn extensions for linear models.

2
econml/sklearn_extensions/model_selection.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Collection of scikit-learn extensions for model selection techniques."""

2
econml/solutions/causal_analysis/__init__.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from ._causal_analysis import CausalAnalysis

2
econml/solutions/causal_analysis/_causal_analysis.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Module for assessing causal feature importance."""

2
econml/tests/dgp.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import abc
import numpy as np

2
econml/tests/test_ate_inference.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import numpy as np

2
econml/tests/test_automated_ml.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import unittest

2
econml/tests/test_bootstrap.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from econml.inference._bootstrap import BootstrapEstimator

2
econml/tests/test_cate_interpreter.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import numpy as np

2
econml/tests/test_causal_analysis.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import unittest

2
econml/tests/test_deepiv.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Tests for `deepiv` module."""

2
econml/tests/test_dml.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import unittest

2
econml/tests/test_dmliv.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import pickle

2
econml/tests/test_dominicks.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import os.path

2
econml/tests/test_dowhy.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import numpy as np

2
econml/tests/test_driv.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from econml.iv.dr import (DRIV, LinearDRIV, SparseLinearDRIV, ForestDRIV, IntentToTreatDRIV, LinearIntentToTreatDRIV,)

2
econml/tests/test_drlearner.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from contextlib import ExitStack

2
econml/tests/test_dynamic_dml.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import unittest
import pytest

2
econml/tests/test_grf_cython.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import unittest

2
econml/tests/test_grf_python.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import unittest

2
econml/tests/test_inference.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import numpy as np

2
econml/tests/test_integration.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from econml.utilities import get_feature_names_or_default

2
econml/tests/test_linear_model.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
"""Tests for linear_model extensions."""

2
econml/tests/test_metalearners.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import numpy as np

5
econml/tests/test_montecarlo.py
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@ -1,8 +1,7 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import unittest
from sklearn.linear_model import LinearRegression, LogisticRegression
from econml.dml import (DML, LinearDML, SparseLinearDML, KernelDML, NonParamDML, CausalForestDML)

2
econml/tests/test_notebooks.py
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import re

2
econml/tests/test_orf.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import numpy as np

2
econml/tests/test_ortho_learner.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
from econml._ortho_learner import _OrthoLearner, _crossfit

2
econml/tests/test_policy_forest.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import unittest

2
econml/tests/test_rscorer.py
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@ -1,4 +1,4 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) PyWhy contributors. All rights reserved.
# Licensed under the MIT License.
import unittest

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