v0.11.0

0.11.0 (2021-07-28)

(sorry for the spam, attempting to correctly update to the right files)

• CausalML surpassed 2K stars!
• We have 3 new community contributors, Jannik (@jroessler), Mohamed (@ibraaaa), and Leo (@lleiou). Thanks for the contribution!

Major Updates

• Make tensorflow dependency optional and add python 3.9 support by @jeongyoonlee (#343)
• Add delta-delta-p (ddp) tree inference approach by @jroessler (#327)
• Add conda env files for Python 3.6, 3.7, and 3.8 by @jeongyoonlee (#324)

Minor Updates

• Fix inconsistent feature importance calculation in uplift tree by @paullo0106 (#372)
• Fix filter method failure with NaNs in the data issue by @manojbalaji1 (#367)
• Add automatic package publish by @jeongyoonlee (#354)
• Fix typo in unit_selection optimization by @jeongyoonlee (#347)
• Fix docs build failure by @jeongyoonlee (#335)
• Convert pandas inputs to numpy in S/T/R Learners by @jeongyoonlee (#333)
• Require scikit-learn as a dependency of setup.py by @ibraaaa (#325)
• Fix AttributeError when passing in Outcome and Effect learner to R-Learner by @paullo0106 (#320)
• Fix error when there is no positive class for KL Divergence filter by @lleiou (#311)
• Add versions to cython and numpy in setup.py for requirements.txt accordingly by @maccam912 (#306)

v0.10.0

0.10.0 (2021-02-19)

• CausalML surpassed 235,000 downloads!
• 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)

v0.9.0

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)

v0.8.0

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)