  Given a causal model of some domain and a particular story that has taken
place in this domain, the problem of actual causation is deciding which of the
possible causes for some effect actually caused it. One of the most influential
approaches to this problem has been developed by Halpern and Pearl in the
context of structural models. In this paper, I argue that this is actually not
the best setting for studying this problem. As an alternative, I offer the
probabilistic logic programming language of CP-logic. Unlike structural models,
CP-logic incorporates the deviant/default distinction that is generally
considered an important aspect of actual causation, and it has an explicitly
dynamic semantics, which helps to formalize the stories that serve as input to
an actual causation problem.
