Two Proposals for Causal Grammars

Thomas L. Griffiths, Joshua B. Tenenbaum

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationCausal Learning
Subtitle of host publicationPsychology, Philosophy, and Computation
PublisherOxford University Press
ISBN (Electronic)9780199958511
ISBN (Print)9780195176803
DOIs
StatePublished - Apr 1 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Psychology(all)

Keywords

  • Bayesian inference
  • Causal learning
  • Causal reasoning
  • First-order logic
  • Generative grammar
  • Intuitive theories
  • Probabilistic models

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  • Cite this

    Griffiths, T. L., & Tenenbaum, J. B. (2010). Two Proposals for Causal Grammars. In Causal Learning: Psychology, Philosophy, and Computation Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195176803.003.0021