Structure and strength in causal induction

Thomas L. Griffiths, Joshua B. Tenenbaum

Research output: Contribution to journalArticle

280 Scopus citations

Abstract

We present a framework for the rational analysis of elemental causal induction-learning about the existence of a relationship between a single cause and effect-based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: The difference between asking whether a causal relationship exists and asking how strong that causal relationship might be. We show that two leading rational models of elemental causal induction, ΔP and causal power, both estimate causal strength, and we introduce a new rational model, causal support, that assesses causal structure. Causal support predicts several key phenomena of causal induction that cannot be accounted for by other rational models, which we explore through a series of experiments. These phenomena include the complex interaction between ΔP and the base-rate probability of the effect in the absence of the cause, sample size effects, inferences from incomplete contingency tables, and causal learning from rates. Causal support also provides a better account of a number of existing datasets than either ΔP or causal power.

Original languageEnglish (US)
Pages (from-to)334-384
Number of pages51
JournalCognitive Psychology
Volume51
Issue number4
DOIs
StatePublished - Dec 1 2005
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Linguistics and Language
  • Artificial Intelligence

Keywords

  • Bayesian models
  • Causal induction
  • Causality
  • Computational modeling
  • Rational analysis

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