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Causal learning : psychology, philosophy, and computation

Author: Alison Gopnik; Laura Schulz
Publisher: Oxford ; New York : Oxford University Press, 2007.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:

Causal Learning provides a compendium of research determining how, in principle, the problem of causal inference and learning can be solved, and a wealth of methods for determining how it is, in  Read more...

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Document Type: Book
All Authors / Contributors: Alison Gopnik; Laura Schulz
ISBN: 0195176804 9780195176803
OCLC Number: 493411582
Description: 1 vol. (X-358 p.) : ill. ; 26 cm
Contents: Introduction / Alison Gopnik and Laura Schulz --
Part I: Causation and intervention --
Interventionist theories of causation in psychological perspective / Jim Woodward --
Infants' causal learning : intervention, observation, imitation / Andrew N. Meltzoff --
Detecting causal structure : the role of intervention in infants' understanding of psychological and physical causal relations / Jessica A. Sommerville --
An interventionist approach to causation in psychology / John Campbell --
Learning from doing : intervention and causal inference / Laura Schulz, Tamar Kushnir, and Alison Gopnik --
Causal reasoning through intervention / York Hagmayer ... [et al.] --
On the importance of causal taxonomy / Christopher Hitchcock --
Part II: Causation and probability --
Introduction to part II : causation and probability / Alison Gopnik and Laura Schulz --
Teaching the normative theory of causal reasoning / Richard Scheines, Matt Easterday, and David Danks --
Interactions between causal and statistical learning / David M. Sobel and Natasha Z. Kirkham --
Beyond covariation : cues to causal structure / David A. Lagnado ... [et al.] --
Theory unification and graphical models in human categorization / David Danks --
Essentialism as a generative theory of classification / Bob Rehder --
Data-mining probabilists or experimental determinists? a dialogue on the principles underlying causal learning in children / Thomas Richardson, Laura Schultz, and Alison Gopnik --
Learning the structure of deterministic systems / Clark Glymour --
Part III: Causation, theories, and mechanisms --
Introduction to part III : causation, theories, and mechanisms / Alison Gopnik and Laura Schulz --
Why represent causal relations? / Michael Strevens --
Causal reasoning as informed by the early development of explanations / Henry M. Wellman and David Liu --
Dynamic interpretations of covariation data / Woo-kyoung Ahn, Jessecae K. Marsh, and Christian C. Luhmann --
Statistical jokes and social effects : intervention and invariance in causal relations / Clark Glymour --
Intuitive theories as grammars for causal inference / Joshua B. Tenenbaum, Thomas L. Griffiths, and Sourabh Niyogi --
Two proposals for causal grammars / Thomas L. Griffiths and Joshua B. Tenenbaum.
Responsibility: edited by Alison Gopnik, Laura Schulz.

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...well worth the effort of reading...a well-developed overview of the current state of research in the field of causal learning. * PsycCritiques *

 
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