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】

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

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

A General Causal Inference Framework by Encoding Generative Modeling

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

가짜연구소 <인과추론과 실무> 프로젝트

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

Taking causal inference to the extreme!

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.

Cusal Inference applied to timeseries, uses an event database to generate a timeseries of the outcome given a sliding window containing events. Useful to add causal outcomes of events into multivariate timeseries forecasting models.

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