  Bayesian Belief Networks have been largely overlooked by Expert Systems
practitioners on the grounds that they do not correspond to the human inference
mechanism. In this paper, we introduce an explanation mechanism designed to
generate intuitive yet probabilistically sound explanations of inferences drawn
by a Bayesian Belief Network. In particular, our mechanism accounts for the
results obtained due to changes in the causal and the evidential support of a
node.
