Causality Python Packages

causallib

A Python package for modular causal inference analysis and model evaluations

econml

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

causal-learn

Causal Discovery in Python. Learning causality from data.

dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

eliot

Eliot: the logging system that tells you *why* it happened

scikit-uplift

:exclamation: uplift modeling in scikit-learn style in python :snake:

lingam

Python package for causal discovery based on LiNGAM.

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.

gcastle

Trustworthy AI related projects

cdt

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

pyphi

A toolbox for integrated information theory.

syntheticcontrolmethods

A Python package for causal inference using Synthetic Controls

dependence-forecastability

General-purpose Python toolkit for horizon-wise forecastability analysis using interchangeable dependence scorers, with AMI/pAMI support, rolling-origin benchmarking, and reproducible reporting.

causetrace

AI coding agent observability and causal tracing for Claude Code, Codex CLI, and OpenCode

causalegm

CausalEGM: an encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies

pybbn

Learning and Inference in Bayesian Belief Networks

causalicp

Python implementation of the Invariant Causal Prediction (ICP) algorithm for causal discovery.

actualcauses

Code for the python model `actualcauses` that implements algorithms for HP-causes identification.

ylearn

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

esa-2scm

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

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

causal-memory-layer

Causal Memory Layer — SDK for recording, auditing, and analyzing causal chains in systems

causallift

CausalLift: Python package for Uplift Modeling for A/B testing and observational data.

causeinfer

Machine learning based causal inference/uplift in Python