Causalml

- CausalML surpassed [235,000 downloads](https://pepy.tech/project/causalml)!
- We have 5 new community contributors, Suraj (surajiyer), Harsh (HarshCasper), Manoj (manojbalaji1), Matthew (maccam912) and Václav (vaclavbelak). Thanks for the contribution!

Major Updates

- Add Policy learner, DR learner, DRIV learner by huigangchen (292)
- Add wrapper for CEVAE, a deep latent-variable and variational autoencoder based model by ppstacy (276)

Minor Updates
- Add propensity_learner to R-learner by jeongyoonlee (297)
- Add BaseLearner class for other meta-learners to inherit from without duplicated code by jeongyoonlee (295)
- Fix installation issue for Shap>=0.38.1 by paullo0106 (287)
- Fix import error for sklearn>= 0.24 by jeongyoonlee (283)
- Fix KeyError issue in Filter method for certain dataset by surajiyer (281)
- Fix inconsistent cumlift score calculation of multiple models by vaclavbelak (273)
- Fix duplicate values handling in feature selection method by manojbalaji1 (271)
- Fix the color spectrum of SHAP summary plot for feature interpretations of meta-learners by paullo0106 (269)
- Add IIA and value optimization related documentation by t-tte (264)
- Fix StratifiedKFold arguments for propensity score estimation by paullo0106 (262)
- Refactor the code with string format argument and is to compare object types, and change methods not using bound instance to static methods by harshcasper (256, 260)

- CausalML won the 1st prize at the poster session in UberML'20
- DoWhy integrated CausalML starting v0.4 ([release note](https://github.com/microsoft/dowhy/releases/tag/v0.4))
- CausalML team welcomes new project leadership, Mert Bay
- We have 4 new community contributors, Mario Wijaya (mwijaya3), Harry Zhao (deeplaunch), Christophe (ccrndn) and Georg Walther (waltherg). Thanks for the contribution!

Major Updates

- Add feature importance and its visualization to UpliftDecisionTrees and UpliftRF by yungmsh (220)
- Add feature selection example with Filter methods by paullo0106 (223)

Minor Updates

- Implement propensity model abstraction for common interface by waltherg (223)
- Fix bug in BaseSClassifier and BaseXClassifier by yungmsh and ppstacy (217, 218)
- Fix parentNodeSummary for UpliftDecisionTrees by paullo0106 (238)
- Add pd.Series for propensity score condition check by paullo0106 (242)
- Fix the uplift random forest prediction output by ppstacy (236)
- Add functions and methods to init for optimization module by mwijaya3 (228)
- Install GitHub Stale App to close inactive issues automatically jeongyoonlee (237)
- Update documentation by deeplaunch, ccrndn, ppstacy(214, 231, 232)

CausalML surpassed [100,000 downloads](https://pepy.tech/project/causalml)! Thanks for the support.

Major Updates

- Add value optimization to `optimize` by t-tte (183)
- Add counterfactual unit selection to `optimize` by t-tte (184)
- Add sensitivity analysis to `metrics` by ppstacy (199, 212)
- Add the `iv` estimator submodule and add 2SLS model to it by huigangchen (201)

Minor Updates

- Add `GradientBoostedPropensityModel` by yungmsh (193)
- Add covariate balance visualization by yluogit (200)
- Fix bug in the X learner propensity model by ppstacy (209)
- Update package dependencies by jeongyoonlee (195, 197)
- Update documentation by jeongyoonlee, ppstacy and yluogit (181, 202, 205)