pymc-labs/pathmc

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.

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.

Python Toolkit for Causal and Probabilistic Reasoning

Causal Inference in Python

A Python library that helps data scientists to infer causation rather than observing correlation.

A set of skills to call your agent Bayes. Thomas Bayes.

StatsPAI is the first Agent-native Python library for causal inference and applied econometrics — unified API, broad cross-method coverage, structured result objects, machine-readable schemas, Skills, an MCP server, and R/Stata parity validation.

A Python package for causal inference in quasi-experimental settings

Causal Discovery in Python. Learning causality from data.