Causal Networks: Semantics and Expressiveness
  Dependency knowledge of the form "x is independent of y once z is known"
invariably obeys the four graphoid axioms, examples include probabilistic and
database dependencies. Often, such knowledge can be represented efficiently
with graphical structures such as undirected graphs and directed acyclic graphs
(DAGs). In this paper we show that the graphical criterion called d-separation
is a sound rule for reading independencies from any DAG based on a causal input
list drawn from a graphoid. The rule may be extended to cover DAGs that
represent functional dependencies as well as conditional dependencies.
