Causal Inference 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.

pgmpy

Python Toolkit for Causal and Probabilistic Reasoning

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.

causalml

Uplift modeling and causal inference with machine learning algorithms

rdrobust

Robust Local Polynomial Methods for RD Designs

scikit-uplift

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

doubleml

DoubleML - Double Machine Learning in Python

lingam

Python package for causal discovery based on LiNGAM.

diff-diff

Difference-in-Differences causal inference in Python. Callaway-Sant'Anna, Synthetic DiD, Honest DiD, event studies. sklearn-like API, validated against R.

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.

morie

Multi-domain Open Research and Inferential Estimation

causalpy

A Python package for causal inference in quasi-experimental settings

tfcausalimpact

Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.

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.

pysyncon

A python module for the synthetic control method

codontrace

CodonTrace Genesis — deterministic research software for digital evolution, causal mechanism auditing, replayable ALife experiments, and evidence-gated AI/evolution studies.

causal-hybrid-control

Physics-structured dynamics with a learned causal residual, controlled by constrained optimal control / MPC.

hypermnesia-mcp

Cross-platform persistent memory MCP for Codex, Gemini CLI, Claude Code, and other local MCP hosts. 36 cited neuroscience mechanisms, local-first SQLite/PostgreSQL, hybrid retrieval, decay-based consolidation, and reproducible benchmarks. Claude adds optional automatic lifecycle hooks.

y0

❓y0 (pronounced "why not?") is for causal inference in Python

cje-eval

Causal Judge Evaluation: calibrate LLM-as-judge scores against oracle labels with valid uncertainty.

cdt

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

bsts-causalimpact

Bayesian CausalImpact for Python — R-compatible Gibbs sampler in Rust (PyO3), 30x faster, no TensorFlow