Theory-Based Causal Inference

Joshua B. Tenenbaum, Thomas L. Griffiths

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

5 Scopus citations

Abstract

People routinely make sophisticated causal inferences unconsciously, effortlessly, and from very little data - often from just one or a few observations. We argue that these inferences can be explained as Bayesian computations over a hypothesis space of causal graphical models, shaped by strong top-down prior knowledge in the form of intuitive theories. We present two case studies of our approach, including quantitative models of human causal judgments and brief comparisons with traditional bottom-up models of inference.

Original languageEnglish (US)
Title of host publicationNIPS 2002
Subtitle of host publicationProceedings of the 15th International Conference on Neural Information Processing Systems
EditorsSuzanna Becker, Sebastian Thrun, Klaus Obermayer
PublisherMIT Press Journals
Pages35-42
Number of pages8
ISBN (Electronic)0262025507, 9780262025508
StatePublished - 2002
Externally publishedYes
Event15th International Conference on Neural Information Processing Systems, NIPS 2002 - Vancouver, Canada
Duration: Dec 9 2002Dec 14 2002

Publication series

NameNIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems

Conference

Conference15th International Conference on Neural Information Processing Systems, NIPS 2002
Country/TerritoryCanada
CityVancouver
Period12/9/0212/14/02

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications
  • Information Systems

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