Structure learning in human causal induction

Joshua B. Tenenbaum, Thomas L. Griffiths

Research output: Chapter in Book/Report/Conference proceedingConference contribution

62 Scopus citations

Abstract

We use graphical models to explore the question of how people learn simple causal relationships from data. The two leading psychological theories can both be seen as estimating the parameters of a fixed graph. We argue that a complete account of causal induction should also consider how people learn the underlying causal graph structure, and we propose to model this inductive process as a Bayesian inference. Our argument is supported through the discussion of three data sets.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000
PublisherNeural information processing systems foundation
ISBN (Print)0262122413, 9780262122412
StatePublished - Jan 1 2001
Externally publishedYes
Event14th Annual Neural Information Processing Systems Conference, NIPS 2000 - Denver, CO, United States
Duration: Nov 27 2000Dec 2 2000

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other14th Annual Neural Information Processing Systems Conference, NIPS 2000
CountryUnited States
CityDenver, CO
Period11/27/0012/2/00

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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