A Decision-Based View of Causality
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:259-270, 1995.
We present a precise definition of cause and effect in terms of a more fundamental notion called unresponsiveness. Our definition departs from the traditional view of causation in that our causal assertions are made relative to a set of decisions. An important consequence of this departure is that we can reason about cause locally, not necessarily attaching a causal explanation to every dependency. Such local reasoning can be beneficial in that, given a set of real decisions to make, it may not be necessary to determine whether some dependencies are causal. Also in this paper, we examine the graphical encoding of causal relationships. We show that ordinary influence diagrams are an inadequate representation of cause, whereas influence diagrams in Howard Canonical Form can always represent cause and effect accurately. In addition, we establish a correspondence between Pearl and Verma’s (1991) causal model and the influence diagram.