Probabilistic Causal Reasoning
  Predicting the future is an important component of decision making. In most
situations, however, there is not enough information to make accurate
predictions. In this paper, we develop a theory of causal reasoning for
predictive inference under uncertainty. We emphasize a common type of
prediction that involves reasoning about persistence: whether or not a
proposition once made true remains true at some later time. We provide a
decision procedure with a polynomial-time algorithm for determining the
probability of the possible consequences of a set events and initial
conditions. The integration of simple probability theory with temporal
projection enables us to circumvent problems that nonmonotonic temporal
reasoning schemes have in dealing with persistence. The ideas in this paper
have been implemented in a prototype system that refines a database of causal
rules in the course of applying those rules to construct and carry out plans in
a manufacturing domain.
