 In our original example, the treatment increased the probability of recovery in each subpopulation, but not it in the population as a whole. Should one approve the drug or not? The causal approach makes it easy to see why one should. The probabilistic relationship between Treatment and Success in the population is an evidential rather than a causal one. Learning that someone took the drug provides evidence about their gender, and this information is relevant to predicting whether they will recover. But this does not tell one about whether the drug is causally efficacious. To learn this, one needs to know how the chances of recovery for individuals in the population would change given an intervention on treatment. This can be determined by conditioning on gender which enables one both to learn the gender-specific effects of the drug, and to derive the average effect in the whole population (using the back-door criterion).