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One obvious diagnosis of the difficulties posed by examples like (2.5.1) and (2.5.2) focuses on the role of causation in explanation. According to this analysis, to explain an outcome we must cite its causes and (2.5.1) and (2.5.2) fail to do this. As Salmon (1989, p.47) puts it, “a flagpole of a certain height causes a shadow of a given length and thereby explains the length of the shadow”. By contrast, “the shadow does not cause the flagpole and consequently cannot explain its height Scientific software ”. Similarly, taking birth control pills does not cause Jones' failure to get pregnant and this is why (2.5.2) fails to be an acceptable explanation. On this analysis, what (2.5.1) and (2.5. 2) show is that a derivation can satisfy the DN criteria and yet fail to identify the causes of an explanandum — when this happens the derivation will fail to be explanatory.
As explained above, advocates of the DN model would not regard this diagnosis as very illuminating, unless accompanied by some account of causation that does not simply take this notion as primitive. (Salmon in fact provides such an account, which we will consider in Section 4.) We should note, however, mathlab that an apparent lesson of (2.5.1) and (2.5.2) is that the regularity account of causation favored by DN theorists is at best incomplete: the occurrence of c, e and the existence of some regularity or law linking them (or x's having property P and x's having property Q and some law linking these) is not a sufficient condition for the truth of the claim that c caused e or x's having P is causally or explanatorily relevant to x's having Q. More generally, if the counterexamples (2.5.1) and (2.5.2) are accepted, it follows that the DN model fails to state sufficient conditions for explanation. Explaining an outcome isn't just a matter of showing that it is nomically expectable.
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