Causal Machine Learning Python Packages

dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

mini-causal

A Python package for measuring the impact of features

causalegm

CausalEGM: an encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies

onlinecml

Online Causal Machine Learning in Python — one observation at a time

causalbootstrapping

CausalBootstrapping is an easy-access implementation and extention of causal bootstrapping (CB) technique for causal analysis. With certain input of observational data, causal graph and variable distributions, CB resamples the data by adjusting the variable distributions which follow intended causal effects.

sccausalvi

Perturbational analysis by causality-aware generative model for single-cell RNA-sequencing data

mechanism-learn

Mechanism-learn is a simple method to deconfound observational data such that any appropriate machine learning model is forced to learn predictive relationships between effects and their causes, despite the potential presence of multiple unknown and unmeasured confounding. The library is compatible with most existing ML deployments.

scmopy

scmopy: Distribution-Agnostic Structural Causal Models Optimization in Python

synthnn

A Python package implementing the synthetic nearest neighbors estimator for panel data causal inference.

causalflows

Causal Normalizing flows in PyTorch