Causal ML: Python package for causal inference machine learning

“Causality” is a complex concept that is based on roots in almost all subject areas and aims to answer the “why” question. Causal inference is one of the important branches of causal analysis, which assumes the existence of relationships between variables and attempts to examine and quantify the act...

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Bibliographic Details
Published in:SoftwareX Vol. 21; p. 101294
Main Authors: Zhao, Yang, Liu, Qing
Format: Journal Article
Language:English
Published: Elsevier B.V 01.02.2023
Elsevier
Subjects:
Causal inference
Causal ML
Machine learning
Causal inference
Causal ML
Machine learning
ISSN:2352-7110, 2352-7110
Online Access:Get full text
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Summary:“Causality” is a complex concept that is based on roots in almost all subject areas and aims to answer the “why” question. Causal inference is one of the important branches of causal analysis, which assumes the existence of relationships between variables and attempts to examine and quantify the actual relationships in the available data. Machine learning (ML) and causal inference are two techniques that emerged and developed separately. However, there is now an intersection between these two fields. Causal ML is a Python package that provides a set of uplift modeling and causal inference methods using machine learning algorithms based on recent research. It gives the user a standard interface that lets them estimate conditional average treatment effects (CATE) or individual treatment effects (ITE) based on experimental observational data.
ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2022.101294