The Book of Why: The New Science of Cause and Effect

by Judea Pearl

Now let’s see how a structural causal model would treat the same data. First, before we even look at the data, we draw a causal diagram (Figure 8.3). The diagram encodes the causal story behind the data, according to which Experience listens to Education and Salary listens to both. In fact, we can already tell something very important just by looking at the diagram. If our model were wrong and EX were a cause of ED, rather than vice versa, then Experience would be a confounder, and matching employees with similar experience would be completely appropriate. With ED as the cause of EX, Experience is a mediator. As you surely know by now, mistaking a mediator for a confounder is one of the deadliest sins in causal inference and may lead to the most outrageous errors. The latter invites adjustment; the former forbids it. FIGURE 8.3. Causal diagram for the effect of education (ED) and experience (EX) on salary (S).