py-why/causal-learn

Health v2 · maintenance only

Adoption not part of the score

1677 stars · 270 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

causal-learn is a Python library for causal discovery, implementing classical and state-of-the-art algorithms to recover causal structure from observational data. It includes constraint-based, score-based, and functional causal model methods, plus utilities like independence tests and graph operations.

Use cases

• learn causal structure from observational data
• run PC or GES causal discovery algorithms in Python
• test conditional independence between variables
• perform Granger causality analysis on time series
• discover latent hidden causal representations
• build custom causal discovery methods with score functions and graph utilities

When to choose

• you need a well-maintained Python port of the Java Tetrad causal discovery algorithms
• you want provable correctness guarantees for structure recovery from observational data
• you need a broad toolkit of causal discovery methods plus building blocks like independence tests in one package

When to avoid

• you need causal effect estimation or treatment effect inference rather than structure discovery
• you require interventional/experimental data analysis or a GUI workflow
• you need a production system with real-time inference rather than offline statistical analysis

Facets

library · maturity active

machine-learning data-science math machine-learning data-science python cross-platform causal-discovery causal-inference causality graph-structure-learning independence-tests granger-causality tetrad algorithms

Member repositories

For agents

markdown · JSON · MCP: product_card(name="py-why/causal-learn")