  We propose a new approach to temporal inference, inspired by the Pearlian
causal inference paradigm - though quite different from Pearl's approach
formally. Rather than using directed acyclic graphs, we make use of factored
sets, which are sets expressed as Cartesian products. We show that finite
factored sets are powerful tools for inferring temporal relations. We introduce
an analog of d-separation for factored sets, conditional orthogonality, and we
demonstrate that this notion is equivalent to conditional independence in all
probability distributions on a finite factored set.
