Welcome to causal-learn’s documentation!

causal-learn is a Python translation and extension of the Tetrad java code. It offers the implementations of up-to-date causal discovery methods as well as simple and intuitive APIs.

Note

This project is under active development. For source code, please kindly refer to our GitHub Repository.

Contents

• Getting started
  • Installation
  • Running examples
  • Benchmarks
  • Contributors

• Search methods
  • Constraint-based causal discovery methods
    • PC
    • FCI
    • CD-NOD
  • Score-based causal discovery methods
    • GES with the BIC score or generalized score
    • Exact Search
  • Causal discovery methods based on constrained functional causal models
    • LiNGAM-based Methods
    • Post-nonlinear causal models
    • Additive noise models
  • Hidden causal representation learning
    • Generalized Independence Noise (GIN) condition-based method
  • Permutation-based causal discovery methods
    • GRaSP
  • Granger causality
    • Linear granger causality

• (Conditional) independence tests
  • Fisher-z test
  • Missing-value Fisher-z test
  • Chi-Square test
  • Kernel-based conditional independence (KCI) test and independence test
  • G-Square test
• Score functions
  • BIC score
  • BDeu score
  • Generalized score with cross validation
  • Generalized score with marginal likelihood
• Utilities
  • Graph operations
  • Evaluations