Symbolic Causal Networks

For a logical database to faithfully represent our beliefs about the world, one should not only insist on its logical consistency but also on its causal consistency. Intuitively, a database is causally inconsistent if it supports belief changes that contradict with our perceptions of causal influences for example, coming to conclude that it must have rained only because the sprinkler was observed to be on. In this paper, we (1) suggest the notion of a causal structure to represent our perceptions of causal influences; (2) provide a formal definition of when a database is causally consistent with a given causal structure; (3) introduce symbolic causal networks as a tool for constructing databases that are guaranteed to be causally consistent; and (4) d iscuss various applications of causal consistency and symbolic causal networks, including nonmonotonic reasoning, Dempster-Shafer reasoning, truth maintenance, and reasoning about actions.