Causallib

Main changes
Two main additions on the model evaluations front.
1. We refactored the whole `evaluation` module, changing the API to be a lot more user friendly, with options to customize the generated plots.
2. We added a whole suite of causal-oriented metrics and scorers, that allow to integrate with scikit-learn's model selection machinery (like `GridSearchCV`, or any other scikit-learn compatible hyperparameter search model), and perform model selection in cross validation.


What's Changed
* limit n_neighbors to n_samples before matching by mmdanziger in https://github.com/IBM/causallib/pull/38
* Evaluation refactoring and interface change by mmdanziger in https://github.com/IBM/causallib/pull/40
* Covariate imbalance scatterplot by edenjenzohar in https://github.com/IBM/causallib/pull/43
* Causal model selection by ehudkr in https://github.com/IBM/causallib/pull/45

New Contributors
* edenjenzohar made their first contribution in https://github.com/IBM/causallib/pull/43

**Full Changelog**: https://github.com/IBM/causallib/compare/v0.8.2...v0.9.0

What's Changed
* `PropensityFeatureStandardization` deepcopy fix by mmdanziger in https://github.com/IBM/causallib/pull/35


**Full Changelog**: https://github.com/IBM/causallib/compare/v0.8.1...v0.8.2

What's Changed
* Fix argument misalignment when passing custom metric to `OutcomeEvaluator` by yoavkt in https://github.com/IBM/causallib/pull/33


**Full Changelog**: https://github.com/IBM/causallib/compare/v0.8.0...v0.8.1

What's Added:
* Causal survival models by liorness in https://github.com/IBM/causallib/pull/25
* Confounder selection module by ehudkr and onkarbhardwaj in https://github.com/IBM/causallib/pull/22
* Targeted Maximum Likelihood Estimator (TMLE) by ehudkr in https://github.com/IBM/causallib/pull/26
* Augmented Inverse Probability Weighting (AIPW) by ehudkr in https://github.com/IBM/causallib/pull/30
* Multiple types of propensity-based features in doubly robust models by ehudkr in https://github.com/IBM/causallib/pull/28 and https://github.com/IBM/causallib/pull/30
* R-learner by Itaymanes in https://github.com/IBM/causallib/pull/24
* X-learner by yoavkt in https://github.com/IBM/causallib/pull/31
* Verbosity control in IPW truncation by liranszlak in https://github.com/IBM/causallib/pull/27


Backward compatibility-breaking changes
* Doubly robust models have been renamed ehudkr in https://github.com/IBM/causallib/pull/28 and https://github.com/IBM/causallib/pull/30
* `DoublyRobustIpFeature` to `PropensityFeatureStandardization`
* `DoublyRobustJoffe` to `WeightedStandardization`
* `DoublyRobustVanilla` to `AIPW`
* Asymmetric propensity truncation in IPW by liranszlak in https://github.com/IBM/causallib/pull/27
* Moving from a single symmetric truncation (`truncate_eps`) to a two-parameter asymmetric truncation (`clip_min, clip_max`)


New Contributors
* onkarbhardwaj made their first contribution in https://github.com/IBM/causallib/pull/22
* Itaymanes made their first contribution in https://github.com/IBM/causallib/pull/24
* liorness made their first contribution in https://github.com/IBM/causallib/pull/25
* liranszlak made their first contribution in https://github.com/IBM/causallib/pull/27
* yoavkt made their first contribution in https://github.com/IBM/causallib/pull/31

**Full Changelog**: https://github.com/IBM/causallib/compare/v0.7.1...v0.8.0

Changes:
* Basic unit testing for plots
* Bug fixes for plotting propensity distribution with non-integer treatment encoding

Changes:
* New models:
* Matching (estimator and preprocessing transformer)
* Overlap Weights
* HEMM
* Weight models now have same `fit()` API as outcome models
* Updated dependency
* Dropped seaborn
* pandas at 0.25
* scikit-learn at 0.25
* Additional fixes and maintenance