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.

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

Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

❗ uplift modeling in scikit-learn style in python 🐍

A toolbox for integrated information theory.

A Python package for modular causal inference analysis and model evaluations

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

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

Code for the Recsys 2018 paper entitled Causal Embeddings for Recommandation.

Python package for causal discovery based on LiNGAM.

A Python package for causal inference using Synthetic Controls

CausalLift: Python package for causality-based Uplift Modeling in real-world business

The official implementation of "Disentangling User Interest and Conformity for Recommendation with Causal Embedding" (WWW '21)

This repository contains the dataset and the PyTorch implementations of the models from the paper Recognizing Emotion Cause in Conversations.

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

Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.

CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

Python tools for regression discontinuity designs

An interpretable framework for inferring nonlinear multivariate Granger causality based on self-explaining neural networks.

A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.