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.

antecedent

Antecedent is a typed causal-analysis compiler whose output is a qualified scientific claim. It keeps identification, structural uncertainty, empirical support, statistical uncertainty, assumptions, implementation evidence and provenance separate and machine-checkable all the way through execution and serialization.

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.

causalegm

A General Causal Inference Framework by Encoding Generative Modeling

sccausalvi

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

scmopy

scmopy: Distribution-Agnostic Structural Causal Models Optimization in Python

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.

synthnn

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

onlinecml

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

causalflows

CausalFlows: A library for Causal Normalizing Flows in Pytorch

metadentify

Meta-/in-context/amortized causal inference for computational identifiability