Year: 2022
Type: software-paper
Abstract: "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.
Source: SoftwareX
Authors: Yang Zhao, Qing Liu
Institutions: Huainan Normal University, Pukyong National University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $600
OpenAlex ID: W4311486892