Causal Discovery Python Packages

pgmpy

Python Toolkit for Causal and Probabilistic Reasoning

causal-learn

Causal Discovery in Python. Learning causality from data.

lingam

Python package for causal discovery based on LiNGAM.

antecedent

Antecedent is a typed causal-analysis compiler whose output is a qualified scientific claim. It keeps identification, structural uncertainty, empirical support, statistical uncertainty, assumptions, implementation evidence and provenance separate and machine-checkable all the way through execution and serialization.

statspai

StatsPAI is the first Agent-native Python library for causal inference and applied econometrics — unified API, broad cross-method coverage, structured result objects, machine-readable schemas, Skills, an MCP server, and R/Stata parity validation.

cdt

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

gcastle

Trustworthy AI related projects

causal-worlds

Generate fictional-but-coherent causal operations worlds (executable sim + time-series + ground-truth causal answer-key) from a natural-language description — for benchmarking causal-discovery and control agents.

cits

Python package for CITS algorithm: Causal inference from time series data

dagma

A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization"

dqtlib

A Data Quality Tool for Agentic BI that tells you the what and the why

copent

Estimating Copula Entropy (Mutual Information), Transfer Entropy (Conditional Mutual Information), and the statistics for multivariate normality test and two-sample test, and change point detection in Python

causalrl

Causal reinforcement learning, organized around Bareinboim's 9-task taxonomy, confounded offline→online RL, POMIS, counterfactual policies, transportability, discovery, imitation, curricula.

avici

Amortized Inference for Causal Structure Learning, NeurIPS 2022

esa-2scm

ESA-2SCM for Causal Discovery: Causal Modeling with Elastic Segmentation-based Synthetic Instrumental Variable

methodlm

Verifiable causal-reasoning harness: pre-registers tests, runs real backdoor adjustment with a robustness value, keeps an honest ledger so any LLM must prove its causal claims. Sibling to OBSERVE.

ylearn

YLearn, a pun of "learn why", is a python package for causal inference

scmopy

scmopy: Distribution-Agnostic Structural Causal Models Optimization in Python

jax-pcmci

High-performance causal discovery using PCMCI algorithms with JAX acceleration

seq2cause

seq2cause: Turns any discrete sequence of events into a causal graph using autoregressive models (LLaMA, GPT, RNN, Mamba).

pytrad

Causal Discovery in Python. Learning causality from data.

andrey-core

A very fast causal discovery package

causationentropy

Implementation of Causation Entropy from Clarkson Center for Complex Systems Science (C3S2)

pypcalg

A Python implementation of the PC algorithm.