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Bayesian Nets and CausalityPhilosophical and Computational Foundations$
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Jon Williamson

Print publication date: 2004

Print ISBN-13: 9780198530794

Published to Oxford Scholarship Online: September 2007

DOI: 10.1093/acprof:oso/9780198530794.001.0001

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DISCOVERING CAUSAL RELATIONSHIPSH

DISCOVERING CAUSAL RELATIONSHIPSH

Chapter:
(p.118) 8 DISCOVERING CAUSAL RELATIONSHIPSH
Source:
Bayesian Nets and Causality
Author(s):

Jon Williamson (Contributor Webpage)

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198530794.003.0008

There are two main proposals for discovering causal relationships: a hypothetico-deductive and an inductive approach. Neither account is fully satisfactory. Various specific approaches are discussed, including constraint-based induction, Bayesian induction, information-theoretic induction, and Shafer's causal conjecturing. These approaches all take the causal Markov condition for granted and so may fail when the condition fails.

Keywords:   hypothetico-deductive, inductive, learning causal nets, constraint-based induction, Bayesian induction, information-theoretic induction, causal conjecturing

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