Inductive Learning, Uncertainty and the Acquisition of Causal Models

Causal models can be regarded as fundamental knowledge bases consisting of rules for generating explanations and predictions. As we all know, such inferences are not free from uncertainty. In an experiment about acquiring causal models by induction, we investigate the impact of the validity of such models on the certainty of inferences. The results indicate that preliminary models are revised in the light of new information, and that the degree of validity considerably influences the certainty of predictions.