Bayesian Generic Priors for Causal Learning

Hongjing Lu

doi:10.1037/a0013256

Bayesian Generic Priors for Causal Learning

2008-10-01

145

OA

AI

DOI：10.1037/a0013256

查看原文

摘要

En 中文

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.

Keyword:

causal learning

Bayesian models

strength judgments

structure judgments

generic priors

Bayesian Generic Priors for Causal Learning

摘要

AI总结

期刊

机构

引用论文