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.

Coz: Causal Profiling

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 …

An index of algorithms for learning causality with data

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

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

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.

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

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.

Generalized Random Forests

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A Python package for causal inference in quasi-experimental settings

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

❗ uplift modeling in scikit-learn style in python 🐍

A Python package for modular causal inference analysis and model evaluations

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