 Much of the subsequent literature on explanation has been motivated by attempts to capture the features of causal or explanatory relevance that appear to be left out of examples like  (4)  and  (5),  typically within the empiricist constraints described above. Wesley Salmon’s statistical relevance (or SR) model (Salmon 1971a) is a very influential attempt to capture these features in terms of the notion of statistical relevance or conditional dependence relationships. Given some class or population \(A\), an attribute \(C\) will be statistically relevant to another attribute \(B\) if and only if \(P(B\pmid A.C) \ne P(B\pmid A)\)—that is, if and only if the probability of \(B\) conditional on \(A\) and \(C\) is different from the probability of \(B\) conditional on \(A\) alone. The intuition underlying the SR model is that statistically relevant properties (or information about statistically relevant relationships) are explanatory and statistically irrelevant properties are not. In other words, the notion of a property making a difference for an explanandum is unpacked in terms of statistical relevance relationships.