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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.

causalegm

A General Causal Inference Framework by Encoding Generative Modeling

onlinecml

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

scmopy

scmopy: Distribution-Agnostic Structural Causal Models Optimization in Python

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.

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.

sccausalvi

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

synthnn

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

causalflows

CausalFlows: A library for Causal Normalizing Flows in Pytorch