Abstract
Causal knowledge enables us to explain past events, to control present environment,
and to predict future outcomes. Over the last decade, causal Bayes nets have been rec-
ognized as a normative framework for causality and used as a psychological model to
account for human causal learning and inference. This article provides an introduction
to causal Bayes nets. According to causal Bayes nets, causal inference can be divided
into three processes: (a) learning the structure of the causal network, (b) learning the
strength of the causal relations, and (c) inferring the effect from the cause or the cause
from the effect. For each process, I describe the predictions of causal Bayes nets, review
experimental results, and suggest future directions. Although there are a few excep-
tions (e.g., Markov violation), most of the results are consistent with the predictions
of causal Bayes nets. The current problems of the Bayesian approach and its future
perspective are discussed.
