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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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Intuitive Theories as Grammars for Causal Inference

Intuitive Theories as Grammars for Causal Inference

(p.301) 19 Intuitive Theories as Grammars for Causal Inference
Causal Learning

Joshua B. Tenenbaum

Thomas L. Griffiths

Sourabh Niyogi

Oxford University Press

This chapter presents a framework for understanding the structure, function, and acquisition of causal theories from a rational computational perspective. Using a “reverse engineering” approach, it considers the computational problems that intuitive theories help to solve, focusing on their role in learning and reasoning about causal systems, and then using Bayesian statistics to describe the ideal solutions to these problems. The resulting framework highlights an analogy between causal theories and linguistic grammars: just as grammars generate sentences and guide inferences about their interpretation, causal theories specify a generative process for events, and guide causal inference.

Keywords:   causal learning, causal reasoning, intuitive theories, Bayesian inference, probabilistic models, generative grammar

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