Constraint-Based Causal Discovery Suite
A four-package Python suite for constraint-based causal discovery — simulation, conditional independence testing, structure learning, and metric / visualisation. Each package is independent and stands on its own; cross-package interoperability is via structural Protocols, not imports.
Package documentation
dagsampler
Configurable DAG / SCM simulator producing synthetic mixed-type data and an optional CI oracle.
cbcd
Constraint-based causal discovery algorithms: PC, FCI, RFCI, anytime-FCI, PCMCI.
citk
Conditional independence test toolkit: FisherZ and Spearman native; KCI / CMIknn / RegressionCI / GCM and others via optional extras.
bnm
DAG / CPDAG / PAG comparison metrics and visualisation: SHD, HD, F1, SID, per-Markov-blanket comparisons.