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Imprecise Bayesian Networks as Causal Models

Kinney, David (2018) Imprecise Bayesian Networks as Causal Models. Information, 9 (9).

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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.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Kinney, Davidd.b.kinney@lse.ac.uk
Keywords: imprecise probabilities; Bayes nets; causal modelling; independence
Subjects: General Issues > Causation
General Issues > Models and Idealization
Depositing User: David Kinney
Date Deposited: 23 Aug 2018 13:33
Last Modified: 23 Aug 2018 13:33
Item ID: 14962
Journal or Publication Title: Information
Publisher: MDPI
Official URL: http://www.mdpi.com/2078-2489/9/9/211
Subjects: General Issues > Causation
General Issues > Models and Idealization
Date: 23 August 2018
Volume: 9
Number: 9
URI: http://philsci-archive.pitt.edu/id/eprint/14962

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