  Directed acyclic graph models with hidden variables have been much studied,
particularly in view of their computational efficiency and connection with
causal methods. In this paper we provide the circumstances under which it is
possible for two variables to be identically equal, while all other observed
variables stay jointly independent of them and mutually of each other. We find
that this is possible if and only if the two variables are `densely connected';
in other words, if applications of identifiable causal interventions on the
graph cannot (non-trivially) separate them. As a consequence of this, we can
also allow such pairs of random variables have any bivariate joint distribution
that we choose. This has implications for model search, since it suggests that
we can reduce to only consider graphs in which densely connected vertices are
always joined by an edge.
