Changelog

0.10.0 (2021-02-18)

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)

0.9.0 (2020-10-23)

  • CausalML won the 1st prize at the poster session in UberML’20

  • DoWhy integrated CausalML starting v0.4 (release note)

  • 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)

0.8.0 (2020-07-17)

CausalML surpassed 100,000 downloads! 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)

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 @paullo0106, @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 @paullo0106

  • 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 [6]

  • Add the CausalTreeRegressor estimator based on Athey and Imbens 2016 [4] (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.