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As we have just seen, the SR model raises a number of interesting questions about the statistical explanation of individual outcomes — questions that are important independently of the details of the SR model itself. This section will abstract away from such questions and focus instead on the root motivation for the SR model. We may take this to consist of two ideas: (i) explanations must cite Equation editor causal relationships and (ii) causal relationships are captured by statistical relevance relationships. Even if (i) is accepted, a fundamental problem with the SR model is that (ii) is false - as a substantial body of work[ 9 ] has made clear, casual relationships are greatly underdetermined by statistical relevance relationships. Consider another example from Salmon (1971): a system in which atmospheric pressure A is a common cause of the occurrence of a storm S and the reading of a barometer B with no causal relationship between B Scientific software and S. Salmon claims that in such a system B and S will be correlated but that B is statistically irrelevant to S given A — i.e. P(S|A.B) = P(S|A). By contrast, (Salmon claims) A remains relevant to S given B - i. e.g., P(S|A.B) 1 P(S|B) . Similarly, S is irrelevant to B given A but A remains relevant B given S. In this way, Salmon's SR model attempts to capture the idea that A is explanatorily (and causally) relevant to S while B is not and that A is explanatorily and causally relevant to B while S is not.