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

Taking causal inference to the extreme!

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

CausalFlows: A library for Causal Normalizing Flows in Pytorch

Predictive State Propensity Subclassification (PSPS): A causal deep learning algoritm in TensorFlow keras

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

RL-based Causal Discovery with Prior Knowledge

Perturbational analysis by causality-aware generative model for single-cell RNA-sequencing data

📦 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

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

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

Análise Avançada de Dados com Causalidade e Aprendizado por Reforço