PyPI · #4487 most downloaded on PyPI

causallib

0.10.0797K downloads/moBiomedSciAI/causallib ↗New releases in your daily digest, changelog included.

A Python package for flexible and modular causal inference modeling

Last release 1 years ago
no release in 18 months
Release timing varies
gaps range from 8 days to 9 months
Nearly every release is documented
notes for 15 of 15 stable releases
Nothing withdrawn
no release was ever pulled
7 years old
16 releases · first in 2019

Release timeline

16 releases since 2019

20192020202120222023202420252026

Releases

0.10.06 Apr 2025
Release v0.10.0 https://pypi.org/project/causallib/0.10.0/
Release v0.10.0 https://pypi.org/project/causallib/0.10.0/

What's Changed

A Positivity filtering (trimming) module by @mmdanziger @liorness @Itaymanes @yoavkt @yishaishimoni in https://github.com/BiomedSciAI/causallib/pull/75

A BICauseTrees implementation by @LucileTerminassian @ehudkr @yishaishimoni @liranszlak @edenjenzohar in https://github.com/BiomedSciAI/causallib/pull/76

Full Changelog: https://github.com/BiomedSciAI/causallib/compare/v0.9.7...v0.10.0

Update 2026/05/07: Tag was redone to fix the stable build on PyPI. Since no content of the actual user-facing package was changed, I did not bump and release a new version. The reason being that changes to ReadTheDocs are of no concern to the users downloading the package from PyPI
Open source →
0.9.731 Jul 2024
Release v0.9.7 https://pypi.org/project/causallib/0.9.7/
Release v0.9.7 https://pypi.org/project/causallib/0.9.7/

What's Changed

Adjust for sklearn>=1.2 (and numpy>=2 and pandas) by @ehudkr in https://github.com/BiomedSciAI/causallib/pull/72

Adjust Simulator to support pandas>=2 and networkx>= dependencies by @d-vct in https://github.com/BiomedSciAI/causallib/pull/71

Update GitHub actions versions and support Python 3.12 testing by @ehudkr in https://github.com/BiomedSciAI/causallib/pull/73

New Contributors

@d-vct made their first contribution in https://github.com/BiomedSciAI/causallib/pull/71

Full Changelog: https://github.com/BiomedSciAI/causallib/compare/v0.9.6...v0.9.7

0.9.625 Oct 2023

Release v0.9.6 https://pypi.org/project/causallib/0.9.6/

What's Changed

Add **kwargs to covariate_imbalance_count_error by @mmdanziger in https://github.com/BiomedSciAI/causallib/pull/64
Adjust for pandas>2 and networkx>3 dependencies by @ehudkr in https://github.com/BiomedSciAI/causallib/pull/65
Test on Python 3.10 and 3.11 by @ehudkr in https://github.com/BiomedSciAI/causallib/pull/65

Full Changelog: https://github.com/BiomedSciAI/causallib/compare/v0.9.5...v0.9.6

Open source →

0.9.522 Jun 2023

Release v0.9.5 https://pypi.org/project/causallib/0.9.5/

What's Changed

Count number of Imbalanced covariates metrics by @ehudkr in https://github.com/BiomedSciAI/causallib/pull/59
sklearn scorer wrapper for propensity models by @ehudkr in https://github.com/BiomedSciAI/causallib/pull/59
Align signature of causal metrics with **kwargs by @ehudkr in https://github.com/BiomedSciAI/causallib/pull/59
Add a name to the time variable Series in NHEFS survival data by @ehudkr in https://github.com/BiomedSciAI/causallib/pull/59

Full Changelog: https://github.com/BiomedSciAI/causallib/compare/v0.9.4...v0.9.5

Open source →

0.9.42 May 2023

Release v0.9.4 https://pypi.org/project/causallib/0.9.4/

What's Changed

NHEFS survival data now respects augmentation parameters by @ehudkr in https://github.com/BiomedSciAI/causallib/pull/57
Allow different covariate specification to weight and outcome models in WeightedStandardizedSurvival by @ehudkr in https://github.com/BiomedSciAI/causallib/pull/57
Allow specifying no covariates (e.g., intercept-only models) in doubly robust (and WeightedStandardizedSurvival) models by @ehudkr in https://github.com/BiomedSciAI/causallib/pull/57
Drop the numpy <1.24.0 dependency restriction by @ehudkr in https://github.com/BiomedSciAI/causallib/pull/57

Full Changelog: https://github.com/BiomedSciAI/causallib/compare/v0.9.3...v0.9.4

Open source →

0.9.219 Feb 2023

Release v0.9.2 https://pypi.org/project/causallib/0.9.2/

What's Changed

Constrain Scikit-learn dependency <1.2 until a solution @ehudkr in https://github.com/BiomedSciAI/causallib/pull/51
Align Matching to WeightEstimator interface by @ehudkr in https://github.com/BiomedSciAI/causallib/pull/53
Optional direct labeling in covariate balance plots by @ehudkr in https://github.com/BiomedSciAI/causallib/pull/54

Full Changelog: https://github.com/BiomedSciAI/causallib/compare/v0.9.1...v0.9.2

Open source →

0.9.029 Sept 2022

Release v0.9.0 https://pypi.org/project/causallib/0.9.0/

Main changes

Two main additions on the model evaluations front.

We refactored the whole evaluation module, changing the API to be a lot more user friendly, with options to customize the generated plots.
We added a whole suite of causal-oriented metrics and scorers, that allow to integrate with scikit-learn's model selection machinery (like GridSearchCV, or any other scikit-learn compatible hyperparameter search model), and perform model selection in cross validation.

What's Changed

limit n_neighbors to n_samples before matching by @mmdanziger in https://github.com/IBM/causallib/pull/38
Evaluation refactoring and interface change by @mmdanziger in https://github.com/IBM/causallib/pull/40
Covariate imbalance scatterplot by @edenjenzohar in https://github.com/IBM/causallib/pull/43
Causal model selection by @ehudkr in https://github.com/IBM/causallib/pull/45

New Contributors

@edenjenzohar made their first contribution in https://github.com/IBM/causallib/pull/43

Full Changelog: https://github.com/IBM/causallib/compare/v0.8.2...v0.9.0

Open source →

0.8.08 Feb 2022

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

What's Added:

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

Backward compatibility-breaking changes

Doubly robust models have been renamed @ehudkr in https://github.com/IBM/causallib/pull/28 and https://github.com/IBM/causallib/pull/30
- DoublyRobustIpFeature to PropensityFeatureStandardization
- DoublyRobustJoffe to WeightedStandardization
- DoublyRobustVanilla to AIPW
Asymmetric propensity truncation in IPW by @liranszlak in https://github.com/IBM/causallib/pull/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 https://github.com/IBM/causallib/pull/22
@Itaymanes made their first contribution in https://github.com/IBM/causallib/pull/24
@liorness made their first contribution in https://github.com/IBM/causallib/pull/25
@liranszlak made their first contribution in https://github.com/IBM/causallib/pull/27
@yoavkt made their first contribution in https://github.com/IBM/causallib/pull/31

Full Changelog: https://github.com/IBM/causallib/compare/v0.7.1...v0.8.0

Open source →

0.7.026 Aug 2021

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

Open source →

0.6.013 Feb 2020

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

Open source →