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Variable Definition and Independent Components

Published online by Cambridge University Press:  01 January 2022

Lorenzo Casini ,
Alessio Moneta  and
Marco Capasso
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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.

Information

Type
Causation
Information
Philosophy of Science , Volume 88 , Issue 5 , December 2021 , pp. 784 - 795
Copyright
Copyright 2021 by the Philosophy of Science Association. All rights reserved.

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