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Changelog

0.4.0 (2019-10-21)

  • Add uplift_tree_plot() to inference.tree to visualize UpliftTreeClassifier by @zhenyuz0500
  • Add the Explainer class to inference.meta to provide feature importances using SHAP and eli5's PermutationImportance by @yungmsh
  • Add bootstrap confidence intervals for the average treatment effect estimates of meta learners by @ppstacy

0.3.0 (2019-09-17)

  • Extend meta-learners to support classification by @t-tte
  • Extend meta-learners to support multiple treatments by @yungmsh
  • Fix a bug in uplift curves and add Qini curves/scores to metrics by @jeongyoonlee
  • Add inference.meta.XGBRRegressor with early stopping and ranking optimization by @yluogit

0.2.0 (2019-08-12)

  • Add optimize.PolicyLearner based on Athey and Wager 2017 :cite:`athey2017efficient`
  • Add the CausalTreeRegressor estimator based on Athey and Imbens 2016 :cite:`athey2016recursive` (experimental)
  • Add missing imports in features.py to enable label encoding with grouping of rare values in LabelEncoder()
  • Fix a bug that caused the mismatch between training and prediction features in inference.meta.tlearner.predict()

0.1.0 (unreleased)

  • Initial release with the Uplift Random Forest, and S/T/X/R-learners.