  Identifying causal relationships from observation data is difficult, in large
part, due to the presence of hidden common causes. In some cases, where just
the right patterns of conditional independence and dependence lie in the
data---for example, Y-structures---it is possible to identify cause and effect.
In other cases, the analyst deliberately makes an uncertain assumption that
hidden common causes are absent, and infers putative causal relationships to be
tested in a randomized trial. Here, we consider a third approach, where there
are sufficient clues in the data such that hidden common causes can be
inferred.
