 The asymmetry of explanation reflects that of causation, which for Salmon is the key to a satisfactory explanatory account. While knowledge of correlations is usually sufficient to bolster prediction, explanation requires more, namely establishing causal relations between events. A favorite example mentioned by Salmon is that of “the barometer and the storm”. Based on the correlation between the behavior of the barometer and the occurrence of storms, one can predict a storm after having observed a sudden drop in the barometer, but nobody would say that the drop in the barometer explains the storm. In similar situations one would look for causal information, such as the sudden drop in atmospheric pressure in the area surrounding the occurrence of the storm. On the one hand, not all statistically relevant properties convey causal information; on the other, statistical correlations themselves invoke an explanation. In view of this, in the course of the Seventies Salmon gradually came to the conclusion that the S-R model cannot substantiate a satisfactory account of explanation. In a paper entitled “Why Ask ‘Why?’?” he maintained: