causal-learn
  • Getting started
  • Search methods
  • (Conditional) independence tests
  • Score functions
  • Utilities
causal-learn
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  • Welcome to causal-learn’s documentation!
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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

Next

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