Causal Inference Python Packages

causallib

A Python package for modular causal inference analysis and model evaluations

pgmpy

Python Toolkit for Causal and Probabilistic Reasoning

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.

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:

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.

causalpy

A Python package for causal inference in quasi-experimental settings

morie

MORIE: multi-domain scientific computing toolkit (Python + R) for observational causal inference, survey methods, and criminal-justice analytics — home of the MRM (Multilevel Reconciliation Methodology) framework.

doubleml

DoubleML - Double Machine Learning in Python

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.

tfcausalimpact

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

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.

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.

y0

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

neuro-cortex-memory

Persistent memory for Claude Code — 36 neuroscience mechanisms, 97 papers. Reproducible via `make reproduce`: LongMemEval-S R@10 98.2% / MRR 0.915 (n=500), LoCoMo R@10 91.5% / MRR 0.805 (n=1982), BEAM-100K retrieval-proxy MRR 0.55. Clean-DB, single-process, production recall path. PostgreSQL + pgvector.

pytorch-tcn

(Realtime) Temporal Convolutions in PyTorch

bsts-causalimpact

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

cje-eval

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

differences

difference-in-differences in Python