FenTechSolutions/CausalDiscoveryToolbox

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1236 stars · 201 forks observed · 2026-08-28

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

A Python library for causal inference in graphs and pairwise settings, implementing many algorithms for graph structure recovery from observational data. It wraps algorithms from packages like bnlearn and pcalg and is built on NumPy, Scikit-learn, PyTorch, and R.

Use cases

• recover causal graph structure from observational data
• run pairwise causal discovery between variables
• apply PC or GES algorithms in Python
• benchmark causal discovery algorithms
• estimate causal dependencies in a dataset
• wrap R bnlearn and pcalg algorithms from Python

When to choose

• you need a broad collection of causal discovery algorithms behind one Python API
• you want to compare or benchmark causal structure recovery methods
• you need pairwise causal inference with GPU-accelerated PyTorch methods

When to avoid

• you need causal effect estimation or do-calculus rather than structure discovery
• you cannot install R and its required packages, since many algorithms depend on them
• you need a maintained GUI or production-grade enterprise support

Facets

library · maturity active

machine-learning data-science math machine-learning data-science python causal-inference causal-discovery graph-structure-recovery causal-graphs pairwise-causality algorithms docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="FenTechSolutions/CausalDiscoveryToolbox")