But it is highly relevant to the construction of theory, and to understanding the less-theoretical statistical approach to the same kinds of questions. One abstract possibility is this: dividing the population into smaller and smaller categories results in a set of rates of outcomes, such as presidential voting preferences, in which the relation between the outcome and the categorization is more or less self-explanatory, or can be explained on the basis of background knowledge that is widely shared, such as the fact that a particular candidate advocates policies favorable to the group in question. In these cases, little in the way of “theory” would be needed. The puzzle is the overall outcome, in this case likely votes for president.
The bulk of the explanation of the outcome is statistical: counting and adding up the size of the categories determines the outcome. In this case, the candidates’ policies are an intervention which is targeted to specific categories in order to influence the total vote. “Theory” plays little role, though some generic background knowledge about what makes people vote is necessary. As we will see in the final section, this is also how other statistical approaches to causal model building proceed.