CausalML: Python Package for Causal Machine Learning

About

CausalML is a Python implementation of algorithms related to causal inference and machine learning. Algorithms combining causal inference and machine learning have been a trending topic in recent years. This package tries to bridge the gap between theoretical work on methodology and practical applications by making a collection of methods in this field available in Python. This paper introduces the key concepts, scope, and use cases of this package.

Huigang Chen, Totte Harinen, Jeong-Yoon Lee, Mike Yung, Zhenyu Zhao · 2020

Related benchmarks

Task	Dataset	Result
Causal effect estimation	Flickr (in-sample)	Epsilon ATE0.6	21
Causal effect estimation	Flickr 1.0 (out-of-sample)	Epsilon ATE1	21
Causal effect estimation	BlogCatalog simulated (In-sample)	ATE Error (Epsilon)1.8	21
Causal effect estimation	BlogCatalog (out-of-sample)	ϵATE6	21
Binary CATE Estimation	Binary-CATE clean data p = 0	RMSE0.191	9
Binary CATE Estimation	Binary-CATE 10% contamination p = 0.10	RMSE1.025	9
Binary CATE Estimation	Binary-CATE 20% contamination p = 0.20	RMSE1.42	9