  We evaluate four computational models of explanation in Bayesian networks by
comparing model predictions to human judgments. In two experiments, we present
human participants with causal structures for which the models make divergent
predictions and either solicit the best explanation for an observed event
(Experiment 1) or have participants rate provided explanations for an observed
event (Experiment 2). Across two versions of two causal structures and across
both experiments, we find that the Causal Explanation Tree and Most Relevant
Explanation models provide better fits to human data than either Most Probable
Explanation or Explanation Tree models. We identify strengths and shortcomings
of these models and what they can reveal about human explanation. We conclude
by suggesting the value of pursuing computational and psychological
investigations of explanation in parallel.
