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Causal LearningPsychology, Philosophy, and Computation$
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Alison Gopnik and Laura Schulz

Print publication date: 2007

Print ISBN-13: 9780195176803

Published to Oxford Scholarship Online: April 2010

DOI: 10.1093/acprof:oso/9780195176803.001.0001

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Why Represent Causal Relations?

Why Represent Causal Relations?

Chapter:
(p.245) 15 Why Represent Causal Relations?
Source:
Causal Learning
Author(s):

Michael Strevens

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195176803.003.0016

Why do we represent the world around us using causal generalizations rather than, say, purely statistical generalizations? Do causal representations contain useful additional information, or are they merely more efficient for inferential purposes? This chapter considers the second kind of answer: it investigates some ways in which causal cognition might aid us not because of its expressive power, but because of its organizational power. Three styles of explanation are considered. The first, building on the work of Reichenbach, points to causal representation as especially efficient for predictive purposes in a world containing certain pervasive patterns of conditional independence. The second, inspired by the work of Woodward and others, finds causal representation to be an excellent vehicle for representing all-important relations of manipulability. The third, based in part on the author's own work, locates the importance of causal cognition in the special role it reserves for information about underlying mechanisms.

Keywords:   causality, causation, mechanisms, manipulation, naive theory, naive physics, naive biology, folk physics, folk biology

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