v0.8.0

Release v0.8.0
https://pypi.org/project/causallib/0.8.0/

What's Added:

• Causal survival models by @liorness in #25
• Confounder selection module by @ehudkr and @onkarbhardwaj in #22
• Targeted Maximum Likelihood Estimator (TMLE) by @ehudkr in #26
• Augmented Inverse Probability Weighting (AIPW) by @ehudkr in #30
• Multiple types of propensity-based features in doubly robust models by @ehudkr in #28 and #30
• R-learner by @Itaymanes in #24
• X-learner by @yoavkt in #31
• Verbosity control in IPW truncation by @liranszlak in #27

Backward compatibility-breaking changes

• Doubly robust models have been renamed @ehudkr in #28 and #30
  • DoublyRobustIpFeature to PropensityFeatureStandardization
  • DoublyRobustJoffe to WeightedStandardization
  • DoublyRobustVanilla to AIPW
• Asymmetric propensity truncation in IPW by @liranszlak in #27
  • Moving from a single symmetric truncation (truncate_eps) to a two-parameter asymmetric truncation (clip_min, clip_max)

New Contributors

• @onkarbhardwaj made their first contribution in #22
• @Itaymanes made their first contribution in #24
• @liorness made their first contribution in #25
• @liranszlak made their first contribution in #27
• @yoavkt made their first contribution in #31

Full Changelog: v0.7.1...v0.8.0

v0.7.1

Release v0.7.1
https://pypi.org/project/causallib/0.7.1/

Changes:

• Basic unit testing for plots
• Bug fixes for plotting propensity distribution with non-integer treatment encoding

v0.7.0

Release v0.7.0
https://pypi.org/project/causallib/0.7.0/

Changes:

• New models:
  • Matching (estimator and preprocessing transformer)
  • Overlap Weights
  • HEMM
• Weight models now have same fit() API as outcome models
• Updated dependency
  • Dropped seaborn
  • pandas at 0.25
  • scikit-learn at 0.25
• Additional fixes and maintenance

v0.6.0

Release v0.6.0
https://pypi.org/project/causallib/0.6.0/

Changes:

• datasets module with toy datasets for causal analysis
  • NHEFS data from Hernan & Robins' book
  • Simulation benchmark data from the ACIC 2016 data challenge
• contrib module for new state-of-the-art outside contributions
  • Adversarial Balancing model
• New implementation for MarginalOutcomeEstimator (formerly UncorrectedEstimator) using WeightEstimator API
• Additional Jupyter Notebook examples
  • NHEFS (Healthcare data)
  • Lalonde (Economic data)
• Additional bug fix and documentation

v0.5.0-beta

Release v0.5.0-beta
https://pypi.org/project/causallib/0.5.0b0/