 For example, if one assumes that Gender is not an effect of Treatment, it cannot be the case that the drug raises the probability of recovery in both males and females, but has no effect on recovery in the general population. This result provides an error theory for why people often find Simpson’s Paradox to be paradoxical in the first place. Specifically, Pearl (2000 [2009], 2014) claims that people conflate observational claims that \(\r{X}\) raises the probability of \(\r{Y}\) with causal claims that doing \(\r{X}\) (versus \(\neg\r{X}\)) would raise the probability of \(\r{Y}\). And assuming that the partitioning variable is not an effect of \(X\), it is impossible for doing \(\r{X}\) to raise the probability of \(\r{Y}\) in all subpopulations, but not in the population as a whole. So Pearl’s explanation of the paradox is that people conflate causal and non-causal expressions, and if the conditional probabilities in the examples are interpreted causally, Simpson’s reversals are impossible.