Rename master branch to main

README.md

@@ -1,4 +1,4 @@
-[![Build Status](https://dev.azure.com/ms/EconML/_apis/build/status/Microsoft.EconML?branchName=master)](https://dev.azure.com/ms/EconML/_build/latest?definitionId=49&branchName=master)
+[![Build Status](https://dev.azure.com/ms/EconML/_apis/build/status/Microsoft.EconML?branchName=main)](https://dev.azure.com/ms/EconML/_build/latest?definitionId=49&branchName=main)
 [![PyPI version](https://img.shields.io/pypi/v/econml.svg)](https://pypi.org/project/econml/)
 [![PyPI wheel](https://img.shields.io/pypi/wheel/econml.svg)](https://pypi.org/project/econml/)
 [![Supported Python versions](https://img.shields.io/pypi/pyversions/econml.svg)](https://pypi.org/project/econml/)
@@ -639,7 +639,7 @@ 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/master/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/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/).
 
 # For Developers
 
@@ -656,12 +656,12 @@ We have added pytest marks to some tests to make it easier to run a subset, and
 
 ## Generating the documentation
 
-This project's documentation is generated via [Sphinx](https://www.sphinx-doc.org/en/master/index.html).  Note that we use [graphviz](https://graphviz.org/)'s 
+This project's documentation is generated via [Sphinx](https://www.sphinx-doc.org/en/main/index.html).  Note that we use [graphviz](https://graphviz.org/)'s 
 `dot` application to produce some of the images in our documentation, so you should make sure that `dot` is installed and in your path.
 
 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/master/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/Microsoft/EconML/tree/main/doc); module documentation is automatically generated by the Sphinx build process.
 
 # Blogs and Publications
 

azure-pipelines.yml

@@ -4,7 +4,7 @@
 # https://docs.microsoft.com/azure/devops/pipelines/languages/python
 
 trigger:
-- master
+- main
 
 jobs:
 - job: 'EvalChanges'

doc/spec/estimation/dml.rst

@@ -589,8 +589,8 @@ Usage Examples
 ==================================
 
 For more extensive examples check out the following notebooks:
-`DML Examples Jupyter Notebook <https://github.com/microsoft/EconML/blob/master/notebooks/Double%20Machine%20Learning%20Examples.ipynb>`_,
-`Forest Learners Jupyter Notebook <https://github.com/microsoft/EconML/blob/master/notebooks/ForestLearners%20Basic%20Example.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>`_.
 
 .. 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/master/notebooks/ForestLearners%20Basic%20Example.ipynb>`_ 
+    `Forest Learners Jupyter notebook <https://github.com/microsoft/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 FAQs
 
 Check out the following Jupyter notebooks:
 
-* `Meta Learners Jupyter Notebook <https://github.com/microsoft/EconML/blob/master/notebooks/Metalearners%20Examples.ipynb>`_ 
-* `Forest Learners Jupyter Notebook <https://github.com/microsoft/EconML/blob/master/notebooks/ForestLearners%20Basic%20Example.ipynb>`_
+* `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>`_
 
 

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/master/notebooks/Orthogonal%20Random%20Forest%20Examples.ipynb>`_ 
-and the `ForestLearners Jupyter notebook <https://github.com/microsoft/EconML/blob/master/notebooks/ForestLearners%20Basic%20Example.ipynb>`_ .
+`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>`_ .
 
 
     .. 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/master/notebooks/Metalearners%20Examples.ipynb>`_. The examples
+`Metalearners Jupyter notebook <https://github.com/Microsoft/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/master/notebooks/Metalearners%20Examples.ipynb>`_.
-    * `DML Examples Jupyter Notebook <https://github.com/microsoft/EconML/blob/master/notebooks/Double%20Machine%20Learning%20Examples.ipynb>`_,
-    * `Forest Learners Jupyter Notebook <https://github.com/microsoft/EconML/blob/master/notebooks/ForestLearners%20Basic%20Example.ipynb>`_.
+    * `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>`_.
 
 
 .. 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/master/notebooks/OrthoIV%20and%20DRIV%20Examples.ipynb>`_.
+`OrthoIV and DRIV Examples Jupyter Notebook <https://github.com/microsoft/EconML/blob/main/notebooks/OrthoIV%20and%20DRIV%20Examples.ipynb>`_.