Causation and decision: On Dawid’s “Decision theoretic foundation of statistical causality”

Abstract In a recent issue of this journal, Philip Dawid (2021) proposes a framework for causal inference that is based on statistical decision theory and that is, in many aspects, compatible with the familiar framework of causal graphs (e.g., Directed Acyclic Graphs (DAGs)). This editorial compares the methodological features of the two frameworks as well as their epistemological basis.

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Year of publication:	2022
Authors:	Pearl, Judea
Published in:	Journal of Causal Inference. - De Gruyter, ISSN 2193-3685, ZDB-ID 2742570-8. - Vol. 10.2022, 1, p. 221-226
Publisher:	De Gruyter
Subject:	directed acyclic graphs \| conditional independence \| potential outcome \| ladder of causation \| causal Bayesian network \| decision theory \| structural causal models \| do -calculus

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Type of publication:	Article
Type of publication (narrower categories):	editorial
Language:	English
Other identifiers:	10.1515/jci-2022-0046 [DOI]
Source:	Other ZBW resources

Persistent link: https://www.econbiz.de/10014610936