 There has been a substantive body of research on learning causal relationships as represented in a causal graph [Spirtes et al. 2000]. Kelly suggested a learning-theoretic analysis of inferring causality where the evidence is provided in the form of observed significant correlations among variables of interest (a modern version of Hume’s “constant conjunctions”). The following inductive method is guaranteed to converge to an empirically adequate causal graph as more and more correlations are observed [Schulte, Luo and Greiner 2007].