Symbolic Causal Networks

Abstract

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) discuss various applications of causal consistency and symbolic causal networks, including nonmonotonic reasoning, Dempster-Shafer reasoning, truth maintenance, and reasoning about actions.