Econml

This release includes the following changes:
* Adds a new double ML class with a non-parametric final stage (170)
* Adds an "honest forest" sklearn-compatible regressor, plus forest-based DML and DR CATE estimators based on it (170)
* Adds classes to support visual interpretation of CATE estimates and policies, along with notebooks demonstrating their use (177)
* Vastly improves the documentation (197)
* Fixes an issue where we used more memory than necessary in a few places (199)

This release includes many major changes.
* Reorganized, expanded, and improved our documentation, including much better content around how to get started with the library (159, 180)
* Enabled the user to specify which inference method to use at `fit`-time.
* Made several enhancements to our Double ML implementation (75)
* Added support for sample weights
* Added support for `statsmodels`-like inference for confidence intervals
* Introduced a more generic base class for orthogonal learners, enabling us to make our DML and DRLearner estimators more consistent with each other and setting the stage for future estimators like DMLIV (132)
* Made several improvements to the DRLearner (137, 167)
* Extended metalearners to handle multiple treatments (rather than only binary treatments) (141)
* Added a debiased lasso implementation to our utilities (138), and used that as the basis for the sparse linear DML estimator (162)
* Enable automatic selection of appropriate models for DML (172)
* Separated the CATE intercept from the CATE coefficients on features for DML (174)

We have also made many improvements around the ergonomics of the library (setting better defaults, making estimators APIs more consistent, etc.), and fixed many minor bugs.

This update provides several minor improvements:
* Adds support for controls to the `NonparametricTwoStageLeastSquares` estimator (**this is a breaking change**)
* Fixes a few minor problems with the Deep IV implementation
* Added support for treatments and outcomes that are vectors rather than 2-D arrays
* Fixes a bug in the the `effect` method
* Enables the `BootstrapEstimator` to work in non-thread-based parallel contexts
* Adds a prototype of the DMLIV estimator (this is not included in the PyPI package because it is a prototype)

This release contains the following changes:
* Improved options for fitting models in Deep IV
* **Breaking change** Reordered arguments to `effect` method, adding default treatments for the common case of a single vector treatment.
* Added a `score` method to the double ML classes
* Improved notebook examples

Welcome to our v0.2 release. This release includes the following:

* New Jupyter notebooks for classes of estimators that were previously missing them
* Performance improvements to the OrthoForest estimators
* Local linear models for OrthoForest leaves
* Refactored Double ML classes to enable future improvements to be made easily
* Enabled discrete treatments to be used with Double ML
* Enabled using controls with the Doubly-Robust Metalearner
* Many minor bugfixes, documentation improvements, and improved tests

Initial SDK release