title: Human causal discovery from observational data.
abstract: Utilizing Bayesian belief networks as a model of causality, we examined medical students' ability to discover causal relationships from observational data. Nine sets of patient cases were generated from relatively simple causal belief networks by stochastic simulation. Twenty participants examined the data sets and attempted to discover the underlying causal relationships. Performance was poor in general, except at discovering the absence of a causal relationship. This work supports the potential for combining human and computer methods for causal discovery.
authors.affiliation: Section of Medical Informatics & Learning Research, University of Pittsburgh, PA 15260, USA.
authors.forename: A I
authors.initials: AI
authors.lastname: Hashem
authors.forename: G F
authors.initials: GF
authors.lastname: Cooper
mesh_terms: D001499:Bayes Theorem; D015984:Causality; D003071:Cognition; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D008960:Models, Psychological; D016571:Neural Networks, Computer; D019370:Observation; D011337:Probability Learning; D013337:Students, Medical / Q000523:psychology*