Econml

This is release contains several major new features as well as a few important breaks in backwards compatibility.

* Introduces Cython implementations of GRF and CausalForestDML, greatly improving the performance of these estimators (341)

* Enables first stage nuisance estimates to be cached, allowing refitting only the final model for ortho learner subclasses (360)

* Enables averaging nuisance estimates over several random splits, resulting in lower variance estimates for ortho learner subclasses (360)

* Adds an `RScorer` class for performing model selection over different CATE estimators (361)

* Enables getting SHAP feature importances for CATE estimates (336, 369)

* More tightly integrates with the `dowhy` library. For instance, the causal graph used by an estimator can be viewed via `est.dowhy.view_model()` (400)

* Improves the display of summaries of inference objects (407)

* __Major Breaking Change__: restructured package organization, moving estimators to more consistent locations; for example, the `IntentToTreatDRIV` estimator is now found at `econml.iv.dr.IntentToTreatDRIV`. For the moment, we also support using the old package organization (e.g. `econml.ortho_iv.IntentToTreatDRIV`), but this is deprecated and will be removed in a subsequent release (370)

* __Breaking Change__: the `n_splits` initializer argument for ortho learner subclasses has been renamed to `cv` to better match sklearn. For the moment, it is still possible to use the name `n_splits`, but this will be removed in a future release (362)

* __Breaking Change__: the base version of the econml package no longer depends on tensorflow or keras (both of which are needed for using DeepIV), or matplotlib (which is needed for rendering tree interpreters). If you need to install these, the first two can be gotten via the econml[tf] extra and matplotlib can be gotten by the econml[plt] extra, or all three libraries can be installed at once via the econml[all] extra (413).

* Many small fixes and improvements (337, 358, 373, 363, 365, 328, 398)

This is the beta for our next major release. It contains several major new features, as well as a few important breaks in backwards compatibility.

* Introduces Cython implementations of GRF and CausalForestDML, greatly improving the performance of these estimators (341)
* Enables first stage nuisance estimates to be cached, allowing refitting only the final model for ortho learner subclasses (360)
* Enables averaging nuisance estimates over several random splits, resulting in lower variance estimates for ortho learner subclasses (360)
* Adds an `RScorer` class for performing model selection over different CATE estimators (361)
* Enables getting SHAP feature importances for CATE estimates (336, 369)
* __Major Breaking Change:__ restructured package organization, moving estimators to more consistent locations; for example, the `IntentToTreatDRIV` estimator is now found at `econml.iv.dr.IntentToTreatDRIV`. For the moment, we also support using the old package organization (e.g. `econml.ortho_iv.IntentToTreatDRIV`), but this is deprecated and will be removed in a subsequent release (370)
* __Breaking Change:__ the `n_splits` initializer argument for ortho learner subclasses has been renamed to `cv` to better match sklearn. For the moment, it is still possible to use the name `n_splits`, but this will be removed in a future release (362)

* Many small fixes and improvements (337, 358, 373, 363, 365, 328)

This is a bugfix release which addresses a few minor issues. The most important issues addressed are:
* Our previous release was incompatible with some commonly used sklearn releases (324)
* Calling fit repeatedly should now return the same results each time when classes are initialized with a fixed random state (325)
* Improvements to how scoring is performed for some classes in `econml.ortho_iv` (325)

This is a major release containing several important improvements.
* Support for python 3.8, tensorflow 2, and sklearn 0.22 and above (210)
* Major enhancements to bootstrap inference, allowing stratified bootstrap for discrete treatments, full inference support \when using bootstrap (including things like `summary` to get nice results tables), and the ability to use pivot bootstrap (now the default) or bootstrap with a parametric normal assumption instead of percentile bootstrap (236, 299)
* Major performance improvements to our OrthoForest classes; in some of our tests the new code is ~10x faster (316)
* Added a CausalForest implementation comparable to grf (316)
* Added full inference support for our Forest classes using bootstrap of little bags (including things like `summary` to get a nice results table) (316)
* Added support for feature importance to ForestDML and ForestDRLearner, and sped them up substantially (306)
* Most estimators now support `inference='auto'` by default during fitting to use a fast recommended form of inference for that estimator (307)
* Added a robust linear model (`StatsModelsRLM`) that can be used as a final stage for DML while supporting inference (307)
* Enabled grouping during cross-fitting for DML and other OrthoLearner classes (273)
* New case study notebooks incorporating DoWhy (255)
* Many bug fixes and small enhancements, such as enabling model serialization (258, 248, 318, 305, 283)

**Upcoming breaking changes**
We have also taken this opportunity to deprecate some aspects of our existing code, and which we will remove completely in a future update to the library. You will now see a warning if you use these features.
* The DML estimators have been renamed to remove the `CateEstimator` suffix; for example, you can now use just `LinearDML` instead of `LinearDMLCateEstimator`. You can continue to use the old names for now, but should move to the new names since the old ones will be removed in a future update.
* The OrthoForest classes have also been renamed to make their names more similar to other classes in our package. `DiscreteTreatmentOrthoForest` is now `DROrthoForest` and `ContinuousTreatmentOrthoForest` is now `DMLOrthoForest`.
* Arguments to `fit` other than `Y` or `T` should now be passed by keyword rather than positionally; previously we had encountered cases where users were passing arguments in the wrong order, which we hope this change will prevent. Again, the old usage will generate a warning and will be removed in a future update.

This is a beta of our next major release, containing the following changes:

* Support for python 3.8, tensorflow 2, and sklearn 0.22 and above (210)
* New case study notebooks incorporating DoWhy (255)
* Many bug fixes and small enhancements, such as enabling model serialization (258, 248)

This is a major release, with the following important changes:
* Richer inference support, including hypothesis testing, p-values, and more when using linear models (203)
* New estimators supporting orthogonal approaches to IV, including DML IV and DR IV (218)
* Experimental support for using Azure Automated Machine Learning for model selection (213)
* Allows the use of bootstrap of little bags for inference with the OrthoForest (214)
* The CATE policy interpreter can now assign treatments to new units based on the learned policy (228)
* Added new Jupyter notebooks illustrating how to use the library for end-to-end scenarios (230)
* Several minor bugfixes (220, 212, 223, 225, 226, 227, 232)