Year: 2021
Type: article
Abstract: In the causal modeling literature, it is well known that ill-defined variables may give rise to ambiguous manipulations. Here, we illustrate how ill-defined variables may also induce mistakes in causal inference when standard causal search methods are applied. To address the problem, we introduce a representation framework, which exploits an independent component representation of the data, and demonstrate its potential for detecting ill-defined variables and avoiding mistaken causal inferences.
Source: Philosophy of Science
DOI: 10.1086/715218
Authors: Lorenzo Casini, Alessio Moneta, Marco Capasso
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W3047729842