Dowhy

* DummyOutcomeRefuter now includes machine learning functions to increase power of the refutation.
* In addition to generating a random dummy outcome, now you can generate a dummyOutcome that is an arbitrary function of confounders but always independent of treatment, and then test whether the estimated treatment effect is zero. This is inspired by ideas from the T-learner.
* We also provide default machine learning-based methods to estimate such a dummyOutcome based on confounders. Of course, you can specify any custom ML method.

* Added a new BootstrapRefuter that simulates the issue of measurement error with confounders. Rather than a simple bootstrap, you can generate bootstrap samples with noise on the values of the confounders and check how sensitive the estimate is.
* The refuter supports custom selection of the confounders to add noise to.

* All refuters now provide confidence intervals and a significance value.

* Better support for heterogeneous effect libraries like EconML and CausalML
* All CausalML methods can be called directly from DoWhy, in addition to all methods from EconML.
* [Change to naming scheme for estimators] To achieve a consistent naming scheme for estimators, we suggest to prepend internal dowhy estimators with the string "dowhy". For example, "backdoor.dowhy.propensity_score_matching". Not a breaking change, so you can keep using the old naming scheme too.
* EconML-specific: Since EconML assumes that effect modifiers are a subset of confounders, a warning is issued if a user specifies effect modifiers outside of confounders and tries to use EconML methods.

* CI and Standard errors: Added bootstrap-based confidence intervals and standard errors for all methods. For linear regression estimator, also implemented the corresponding parametric forms.

* Convenience functions for getting confidence intervals, standard errors and conditional treatment effects (CATE), that can be called after fitting the estimator if needed

* Better coverage for tests. Also, tests are now seeded with a random seed, so more dependable tests.

Thanks to Tanmay-Kulkarni101 and Arshiaarya for their contributions!

This release includes many major updates:

* (BREAKING CHANGE) The CausalModel import is now simpler: "from dowhy import CausalModel"
* Multivariate treatments are now supported.
* Conditional Average Treatment Effects (CATE) can be estimated for any subset of the data. Includes integration with EconML--any method from EconML can be called using DoWhy through the estimate_effect method (see example notebook).
* Other than CATE, specific target estimands like ATT and ATC are also supported for many of the estimation methods.
* For reproducibility, you can specify a random seed for all refutation methods.
* Multiple bug fixes and updates to the documentation.


Includes contributions from j-chou, ktmud, jrfiedler, shounak112358, Lnk2past. Thank you all!

This release implements the four steps of causal inference: model, identify, estimate and refute. It also includes a pandas.DataFrame extension for causal inference and the do-sampler.