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Open AccessArticle

Imprecise Bayesian Networks as Causal Models

Department of Philosophy, Logic and Scientific Method, London School of Economics, London WC2A 2AE, UK
Information 2018, 9(9), 211; https://doi.org/10.3390/info9090211
Received: 12 July 2018 / Revised: 15 August 2018 / Accepted: 20 August 2018 / Published: 23 August 2018
(This article belongs to the Special Issue Probabilistic Causal Modelling in Intelligent Systems)
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. View Full-Text
Keywords: imprecise probabilities; Bayes nets; causal modelling; independence imprecise probabilities; Bayes nets; causal modelling; independence
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Kinney, D. Imprecise Bayesian Networks as Causal Models. Information 2018, 9, 211.

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