Update copyright and links to point to PyWhy

Signed-off-by: Keith Battocchi <[email protected]>

Author: kbattocchi

LICENSE

@@ -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

README.md

@@ -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
 
@@ -692,15 +692,15 @@ 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:
 
 ```
 @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}
 }

doc/conf.py

@@ -21,8 +21,8 @@
 # -- 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 @@
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
     (root_doc, 'econml.tex', 'econml Documentation',
-     'Microsoft Research', 'manual'),
+     'PyWhy contributors', 'manual'),
 ]
 
 

doc/spec/estimation/dml.rst

@@ -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
 

doc/spec/estimation/dr.rst

@@ -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>`_
 
 

doc/spec/estimation/forest.rst

@@ -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::

doc/spec/estimation/metalearners.rst

@@ -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::

doc/spec/estimation/orthoiv.rst

@@ -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>`_.

doc/spec/motivation.rst

@@ -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>`__.

econml/__init__.py

@@ -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',