  The paper formalizes constraint-based structure learning of the "true" causal
graph from observed data when unobserved variables are also existent. We define
a "generic" structure learning algorithm, which provides conditions that, under
the faithfulness assumption, the output of all known exact algorithms in the
literature must satisfy, and which outputs graphs that are Markov equivalent to
the causal graph. More importantly, we provide clear assumptions, weaker than
faithfulness, under which the same generic algorithm outputs Markov equivalent
graphs to the causal graph. We provide the theory for the general class of
models under the assumption that the distribution is Markovian to the true
causal graph, and we specialize the definitions and results for structural
causal models.
