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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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Two Proposals for Causal Grammars

Two Proposals for Causal Grammars

Chapter:
(p.323) 20 Two Proposals for Causal Grammars
Source:
Causal Learning
Author(s):

Thomas L. Griffiths

Joshua B. Tenenbaum

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195176803.003.0021

A causal theory can be thought of as a grammar that generates events, and that can be used to parse events to identify underlying causal structure. This chapter considers what the components of such a grammar might be — the analogues of syntactic categories and the rules that relate them in a linguistic grammar. It presents two proposals for causal grammars. The first asserts that the variables which describe events can be organized into causal categories, and allows relationships between those categories to be expressed. The second uses a probabilistic variant of first-order logic in order to describe the ontology and causal laws expressed in an intuitive theory. This chapter illustrates how both kinds of grammar can guide causal learning.

Keywords:   causal learning, causal reasoning, intuitive theories, Bayesian inference, probabilistic models, generative grammar, first-order logic

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