 PR has some advantages over the simplest version of a regularity theory of causation (discussed in  Section 1.1  above). PR is compatible with imperfect regularities: C may raise the probability of E even though instances of C are not invariably followed by instances of E. Moreover, PR addresses the problem of relevance: if C is a cause of E, then C makes a difference for the probability of E. But as it stands, PR does not address either the problem of asymmetry, or the problem of spurious correlations. PR does not address the problem of asymmetry because probability-raising turns out to be symmetric: \(\PP(E \mid C) \gt \PP(E \mid {\nsim}C)\), if and only if \(\PP(C \mid E) \gt \PP(C \mid {\nsim}E)\). Thus PR by itself cannot determine whether C is the cause of E or vice versa. PR also has trouble with spurious correlations. If C and E are both caused by some third factor, A, then it may be that \(\PP(E \mid C) \gt \PP(E \mid {\nsim}C)\) even though C does not cause E. This is the situation shown in  Figure 1  above. Here, C is the drop in the level of mercury in a barometer, and E is the occurrence of a storm. Then we would expect that \(\PP(E \mid C) \gt \PP(E \mid {\nsim}C)\). In this case, atmospheric pressure is referred to as a confounding factor.