# 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.

# Contents

- Getting started
  - Installation
  - Running examples

- Search methods
  - Constrained-based causal discovery methods
    - PC
    - FCI
  - 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
  - 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

# Indices and tables

- Index

- Module Index

- Search Page