 Pearl (2014) presents several reasons supporting his analysis of the paradox. First, he argues that were the surprise resulting from the paradox to be the result of a mere mathematical error, this could neither account for why the paradox “has captured the fascination of statisticians, mathematicians, and philosophers for over a century” (2014: 9) nor for the difficulty that reasoners have in avoiding the error even once they’ve been made aware of it. Only by means of a causal semantics can one demonstrate that Simpson’s reversals cannot occur when the conditional probabilities are interpreted causally. Second, he points to Simpson’s (1951) observation that judgments about whether the aggregated or non-aggregated population is relevant for evaluating the correlations depends on the story behind the what the frequencies represent. Pearl accounts for this story-relativity by showing that whether one should partition a population is decided not by the probabilities but rather by the causal model generating the probabilities. These causal models  cannot be distinguished by conditional probabilities alone.