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Changelog

0.7.1 (2020-05-07)

Special thanks to our new community contributor, Katherine (@khof312)!

Major Updates

  • Adjust matching distances by a factor of the number of matching columns in propensity score matching by @yungmsh (#157)
  • Add TMLE-based AUUC/Qini/lift calculation and plotting by @ppstacy (#165)

Minor Updates

  • Fix typos and update documents by @paulluo0106, @khof312, @jeongyoonlee (#150, #151, #155, #163)
  • Fix error in UpliftTreeClassifier.kl_divergence() for pk == 1 or 0 by @jeongyoonlee (#169)
  • Fix error in BaseRRegressor.fit() without propensity score input by @jeongyoonlee (#170)

0.7.0 (2020-02-28)

Special thanks to our new community contributor, Steve (@steveyang90)!

Major Updates

  • Add a new nn inference submodule with DragonNet implementation by @yungmsh
  • Add a new feature selection submodule with filter feature selection methods by @zhenyuz0500

Minor Updates

  • Make propensity scores optional in all meta-learners by @ppstacy
  • Replace eli5 permutation importance with sklearn's by @yluogit
  • Replace ElasticNetCV with LogisticRegressionCV in propensity.py by @yungmsh
  • Fix the normalized uplift curve plot with negative ATE by @jeongyoonlee
  • Fix the TravisCI FOSSA error for PRs from forked repo by @steveyang90
  • Add documentation about tree visualization by @zhenyuz0500

0.6.0 (2019-12-31)

Special thanks to our new community contributors, Fritz (@fritzo), Peter (@peterfoley) and Tomasz (@TomaszZamacinski)!

  • Improve UpliftTreeClassifier's speed by 4 times by @jeongyoonlee
  • Fix impurity computation in CausalTreeRegressor by @TomaszZamacinski
  • Fix XGBoost related warnings by @peterfoley
  • Fix typos and improve documentation by @peterfoley and @fritzo

0.5.0 (2019-11-26)

Special thanks to our new community contributors, Paul (@paullo0106) and Florian (@FlorianWilhelm)!

  • Add TMLELearner, targeted maximum likelihood estimator to inference.meta by @huigangchen
  • Add an option to DGPs for regression to simulate imbalanced propensity distribution by @huigangchen
  • Fix incorrect edge connections, and add more information in the uplift tree plot by @paulluo0106
  • Fix an installation error related to Cython and numpy by @FlorianWilhelm
  • Drop Python 2 support from setup.py by @jeongyoonlee
  • Update causaltree.pyx Cython code to be compatible with scikit-learn>=0.21.0 by @jeongyoonlee

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 athey2017efficient
  • Add the CausalTreeRegressor estimator based on Athey and Imbens 2016 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.