# FenTechSolutions/CausalDiscoveryToolbox

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

Repository: https://github.com/FenTechSolutions/CausalDiscoveryToolbox
Canonical: https://ross.abutalabs.com/products/causaldiscoverytoolbox
Homepage: https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/index.html
Language: Python
License: MIT
License Family: permissive
Topics: causal-inference, graph, causality, causal-models, algorithm, machine-learning, graph-structure-recovery, python, causal-discovery, toolbox, inference
Last push: 2025-10-13T07:40:06+00:00

## Health v2 (maintenance only)
Score: 53/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 46, release rhythm 35, longevity 100
- inputs: {"age_days": 3381, "days_push": 324, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1236, forks 201 (observed 2026-08-28T04:04:05.045436+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: machine-learning, data-science, math
- domain: machine-learning, data-science
- platform: python
- tags: causal-inference, causal-discovery, graph-structure-recovery, causal-graphs, pairwise-causality, algorithms, docker

## Member repositories
- FenTechSolutions/CausalDiscoveryToolbox (main) score 53

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:05.045436+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:21:56.008011+00:00, confidence not recorded.
  - readme: https://github.com/FenTechSolutions/CausalDiscoveryToolbox (fetched 2026-08-28T04:04:05.045436+00:00, sha 8f508a6f7738)
  - homepage: https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/index.html (fetched 2026-08-29T12:21:26.056648+00:00, sha 3e96b70cb609)
- Data as of 2026-08-30T08:39:29.467469+00:00.