  Markov networks and Bayesian networks are effective graphic representations
of the dependencies embedded in probabilistic models. It is well known that
independencies captured by Markov networks (called graph-isomorphs) have a
finite axiomatic characterization. This paper, however, shows that
independencies captured by Bayesian networks (called causal models) have no
axiomatization by using even countably many Horn or disjunctive clauses. This
is because a sub-independency model of a causal model may be not causal, while
graph-isomorphs are closed under sub-models.
