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.

Uplift modeling and causal inference with machine learning algorithms

Next generation of automated data exploratory analysis and visualization platform.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Coz: Causal Profiling

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.

An index of algorithms for learning causality with data

Python library for Causal AI

A Python library that helps data scientists to infer causation rather than observing correlation.

推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.

Causal Discovery in Python. Learning causality from data.

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

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

A Python package for causal inference in quasi-experimental settings

Trustworthy AI related projects

A curated list of causal inference libraries, resources, and applications.

Generalized Random Forests

Learn about Machine Learning and Artificial Intelligence

Causal Inference for the Brave and True的中文翻译版。全部代码基于Python，适用于计量经济学、量化社会学、策略评估等领域。英文版原作者：Matheus Facure

Zero-trust Opensource Network Management and Observability Platform

A Python package for modular causal inference analysis and model evaluations

:exclamation: uplift modeling in scikit-learn style in python :snake:

Must-read papers and resources related to causal inference and machine (deep) learning

DoubleML - Double Machine Learning in Python

Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.

We will keep updating the paper list about machine learning + causal theory. We also internally discuss related papers between NExT++ (NUS) and LDS (USTC) by week.

Causal Inference for The Brave and True 책의 한국어 번역 자료입니다.

Python package for causal discovery based on LiNGAM.

Statistical Rethinking (2nd ed.) with NumPyro

:arrow_lower_left: :arrow_lower_right: An R package for working with causal directed acyclic graphs (DAGs)