Learning the Form of Causal Relationships Using Hierarchical Bayesian Models

Christopher Lucas, Thomas L. Griffiths

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.
Original languageEnglish
Pages (from-to)113-147
Number of pages35
JournalCognitive Science: A Multidisciplinary Journal
Issue number1
Publication statusPublished - 2010

Keywords / Materials (for Non-textual outputs)

  • Causal reasoning, Bayesian networks, Bayesian models, Hierarchical models, Rational inference, Structure learning, Human experimentation, Computer simulation


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