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.