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 | Rank | |
|---|---|---|---|---|
| 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 |
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