  In recent years the possibility of relaxing the so-called Faithfulness
assumption in automated causal discovery has been investigated. The
investigation showed (1) that the Faithfulness assumption can be weakened in
various ways that in an important sense preserve its power, and (2) that
weakening of Faithfulness may help to speed up methods based on Answer Set
Programming. However, this line of work has so far only considered the
discovery of causal models without latent variables. In this paper, we study
weakenings of Faithfulness for constraint-based discovery of semi-Markovian
causal models, which accommodate the possibility of latent variables, and show
that both (1) and (2) remain the case in this more realistic setting.
