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.

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

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

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

A Python package for modular causal inference analysis and model evaluations

❗ uplift modeling in scikit-learn style in python 🐍

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

Python package for causal discovery based on LiNGAM.

A toolbox for integrated information theory.

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

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

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

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

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

A Python package for causal inference using Synthetic Controls

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

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

Official code of "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)

[CVPR 2021] Counterfactual VQA: A Cause-Effect Look at Language Bias

[Experimental] Global causal discovery algorithms