Representing causal information about a probabilistic process

We study causal information about probabilistic processes, i.e., information about why events occur. A language is developed in which such information can be formally represented and we investigate when this suffices to uniquely characterize the probability distribution that results from such a process. We examine both detailed representations of temporal aspects and representations in which time is implicit. In this last case, our logic turns into a more fine-grained version of Pearl's approach to causality. We relate our logic to certain probabilistic logic programming languages, which leads to a clearer view on the knowledge representation properties of these language. We show that our logic induces a semantics for disjunctive logic programs, in which these represent non-deterministic processes. We show that logic programs under the well-founded semantics can be seen as a language of deterministic causality, which we relate to McCain & Turner's causal theories.acceptance rate=44%status: publishe