The Cognitive Processing of Causal Knowledge
  There is a brief description of the probabilistic causal graph model for
representing, reasoning with, and learning causal structure using Bayesian
networks. It is then argued that this model is closely related to how humans
reason with and learn causal structure. It is shown that studies in psychology
on discounting (reasoning concerning how the presence of one cause of an effect
makes another cause less probable) support the hypothesis that humans reach the
same judgments as algorithms for doing inference in Bayesian networks. Next, it
is shown how studies by Piaget indicate that humans learn causal structure by
observing the same independencies and dependencies as those used by certain
algorithms for learning the structure of a Bayesian network. Based on this
indication, a subjective definition of causality is forwarded. Finally, methods
for further testing the accuracy of these claims are discussed.
