Bayesian generic priors for causal learning

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Bayesian generic priors for causal learning (2008)

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by Hongjing Lu , Alan L. Yuille , Mimi Liljeholm , Patricia W. Cheng , Keith J. Holyoak

Venue:	Psychological Review
Citations:	34 - 5 self

BibTeX

@ARTICLE{Lu08bayesiangeneric,
    author = {Hongjing Lu and Alan L. Yuille and Mimi Liljeholm and Patricia W. Cheng and Keith J. Holyoak},
    title = {Bayesian generic priors for causal learning},
    journal = {Psychological Review},
    year = {2008},
    pages = {984}
}

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Abstract

The article presents a Bayesian model of causal learning that incorporates generic priors—systematic assumptions about abstract properties of a system of cause–effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes—causes that are few in number and high in their individual powers to produce or prevent effects. The SS power model couples these generic priors with a causal generating function based on the assumption that unobservable causal influences on an effect operate independently (P. W. Cheng, 1997). The authors tested this and other Bayesian models, as well as leading nonnormative models, by fitting multiple data sets in which several parameters were varied parametrically across multiple types of judgments. The SS power model accounted for data concerning judgments of both causal strength and causal structure (whether a causal link exists). The model explains why human judgments of causal structure (relative to a Bayesian model lacking these generic priors) are influenced more by causal power and the base rate of the effect and less by sample size. Broader implications of the Bayesian framework for human learning are discussed.

Keyphrases

bayesian model causal learning bayesian generic prior generic prior s power model causal structure sample size abstract property causal link causal strength causal power individual power base rate effect operate unobservable causal influence nonnormative model human learning generic prior systematic assumption favor sparse multiple data set several parameter multiple type cause effect relation bayesian framework human judgment

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