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.

A resource list for causality in statistics, data science and physics

🛠 How to Apply Causal ML to Real Scene Modeling？How to learn Causal ML？【✔从Causal ML到实际场景的Uplift建模】

Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.

Scikit-learn compatible decision trees beyond those offered in scikit-learn

A General Causal Inference Framework by Encoding Generative Modeling

🎯 📕 Targeted Learning in R: A Causal Data Science Handbook

가짜연구소 <인과추론과 실무> 프로젝트

Taking causal inference to the extreme!

Official PyTorch Implementation for "Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection" in CVPR 2024

Implementations of var-sortability, sortnregress, and chain-orientation as presented in the article "Beware of the Simulated DAG": https://arxiv.org/abs/2102.13647.

📦 R/medoutcon: Efficient Causal Mediation Analysis with Natural and Interventional Direct/Indirect Effects

Official implementation for ICML23 paper: Which Invariance Should We Transfer? A Causal Minimax Learning Approach

Causal Discovery with Prior Knowledge

This library provides packages on DoubleML / Causal Machine Learning and Neural Networks in Python for Simulation and Case Studies.

Collection and implementation of a variety of machine learning code examples (notebooks and Python scripts) and projects.

Code for Causal GAIL. "Learning human driving behaviors with sequential causal imitation learning", AAAI-22.

ImpactFlow is a Python Library for decision modeling based on causal decision models - in which levers and external factors of decisions feed into outcomes.

Treatment evaluation in presence of large number of covariates or treatment heterogeneity through Machine Learning methods

Python package for SCM-based simulation of gene perturbation data and benchmarking of causal structure learning algorithms.