Bayesian Nets and Causality: Philosophical and Computational Foundations

Hardcover | December 23, 2004

byJon Williamson

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Bayesian nets are widely used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. But many philosophers have criticised and ultimately rejected the central assumptionon which such work is based - the Causal Markov Condition. So should Bayesian nets be abandoned? What explains their success in artificial intelligence?This book argues that the Causal Markov Condition holds as a default rule: it often holds but may need to be repealed in the face of counterexamples. Thus Bayesian nets are the right tool to use by default but naively applying them can lead to problems. The book develops a systematic account ofcausal reasoning and shows how Bayesian nets can be coherently employed to automate the reasoning processes of an artificial agent.The resulting framework for causal reasoning involves not only new algorithms but also new conceptual foundations. Probability and causality are treated as mental notions - part of an agent's belief state. Yet probability and causality are also objective - different agents with the same backgroundknowledge ought to adopt the same or similar probabilistic and causal beliefs. This book, aimed at researchers and graduate students in computer science, mathematics and philosophy, provides a general introduction to these philosophical views as well as an exposition of the computational techniquesthat they motivate.

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From the Publisher

Bayesian nets are widely used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. But many philosophers have criticised and ultimately rejected the central assumptionon which such work is based - the Causal Mar...

Jon Williamson is at the Department of Philosophy, Logic, and Scientific Method, London School of Economics, London

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Format:HardcoverDimensions:250 pages, 9.21 × 6.14 × 0.74 inPublished:December 23, 2004Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:019853079X

ISBN - 13:9780198530794

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Table of Contents

IntroductionProbabilityBayesian NetsCausal Nets: Foundational ProblemsObjective BayesianismTwo-Stage Bayesian NetsCausalityDiscovering Causal RelationshipsEpistemic CausalityRecursive CausalityLogicLanguage ChangeReferencesIndex

Editorial Reviews

`This book marks an important development ... the whole exposition is something of a tour de force, Williamson's book will come to be recognised as a major contribution 'Colin Howson, Chair in Philosophy of Science, London School of Economics