 First of all, a theory of probabilistic causal explanation requires that a distinction can be made between statistical and causal relevance. The tool for that purpose is the screening off relation, defined as follows. Going back to the example of the barometer and the storm, let B stand for the barometer drop, S for the storm, and P for the drop in atmospheric pressure. There is a correlation between B and S, so that \(p (S \mid B) \gt p (S)\)—namely B is statistically relevant to S. But if we take into account P, we see that \(p (S \mid P \amp B) = p (S \mid P)\), namely B becomes irrelevant to S in the presence of P. This means that P screens off B from S. It should not pass unnoticed that the screening off relation is asymmetrical; in our example B does not screen off P from S, for \(p (S \mid P \amp B) \ne p (S \mid B)\). Salmon formulates a screening off rule requiring that those properties which are screened off by other properties are removed from the reference class. This makes screening off the canon guiding the search for homogeneous reference classes. Moreover, by virtue of its asymmetric character the screening off relation forges a bridge between statistical and causal relevance, obviously taken in a probabilistic sense.