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Imprecise Bayesian networks as causal models

Kinney, David (2018) Imprecise Bayesian networks as causal models. Information, 9 (9). p. 211. ISSN 2078-2489

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Identification Number: 10.3390/info9090211

Abstract

This article considers the extent to which Bayesian networks with imprecise probabilities, which are used in statistics and computer science for predictive purposes, can be used to represent causal structure. It is argued that the adequacy conditions for causal representation in the precise context—the Causal Markov Condition and Minimality—do not readily translate into the imprecise context. Crucial to this argument is the fact that the independence relation between random variables can be understood in several different ways when the joint probability distribution over those variables is imprecise, none of which provides a compelling basis for the causal interpretation of imprecise Bayes nets. I conclude that there are serious limits to the use of imprecise Bayesian networks to represent causal structure.

Item Type: Article
Official URL: https://www.mdpi.com/journal/information
Additional Information: © 2018 The Author © CC BY 4.0
Divisions: Philosophy, Logic and Scientific Method
Subjects: Q Science > QA Mathematics
Sets: Departments > Philosophy, Logic and Scientific Method
Date Deposited: 12 Oct 2018 11:02
Last Modified: 20 Feb 2019 06:54
URI: http://eprints.lse.ac.uk/id/eprint/90417

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