 Thus, whether one should partition the population based on a factor in order to identify a particular causal relationship does not depend only on the statistical distribution, but crucially on one’s causal background assumptions. Suppose that one was considering an intermediate variable such as Pregnancy in Hesslow’s (1976) example. Recall that in the example birth control influences thrombosis both positively via a blood chemical and negatively by reducing one’s chance of getting pregnant. This case is shown in  Figure 3  and contrasted with our running example where the third variable is a confounding factor. In order to identify the effect of birth control on thrombosis, it is crucial that one does not condition on pregnancy. If there are no unmeasured common causes of birth control and thrombosis, then a probability-raising relationship between birth control and thrombosis in the population as a whole would reliably indicate that taking birth control pills promotes thrombosis.