Econml

This is the beta for our next 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)
* The ability to use bootstrap of little bags for inference with the OrthoForest (214)
* Several minor bugfixes (220, 212)

This release fixes a few bugs:
* Improves the selection of alphas when using debiased lasso (211)
* Fixes an issue with weight clipping in non-parametric DML (208)
* Enables DRLearner subclasses to handle Y arrays with shape (n,1) instead of just (n,) (209)

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