  It is well known that there may be many causal explanations that are
consistent with a given set of data. Recent work has been done to represent the
common aspects of these explanations into one representation. In this paper, we
address what is less well known: how do the relationships common to every
causal explanation among the observed variables of some DAG process change in
the presence of latent variables? Ancestral graphs provide a class of graphs
that can encode conditional independence relations that arise in DAG models
with latent and selection variables. In this paper we present a set of
orientation rules that construct the Markov equivalence class representative
for ancestral graphs, given a member of the equivalence class. These rules are
sound and complete. We also show that when the equivalence class includes a
DAG, the equivalence class representative is the essential graph for the said
DAG
