by Simpson’s paradox and perspectival realism Which causal relationships we see depend on which model we use and its conceptual/causal articulation; which model is bestdepends on our purposes and pragmatic interests. Take the case of Simpson’s paradox, which can be described as the situation in which conditional probabilities (often related to causal relations) are opposite for subpopulations than for the whole population. Let academic salaries be higher for economists than for sociologists, and let salaries within each group be higher for women than for men. But let there be twice as many men than women in economics and twice as many women than men in sociology. By construction, the average salary of women is higher than that for men in each group; yet, for the right values of the different salaries, women are paid less on average, taking both groups together. [Example: Economics — 2 men earn 100$, 1 woman 101$; Sociology — 1 man earn 90$, 2 women 91$. Average female earning: (101 + 2×91)/3 = 94.3; Average male earning: (2×100 + 90)/3 = 96.6 — LPS] An aggregate model leads to the conclusion that that being female causes a lower salary. We might feel an uneasiness with such a model, since I have already filled in the details that show more precisely why the result comes about.
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Which causal relationships we see depend on which model we use and its conceptual/causal articulation; which model is bestdepends on our purposes and pragmatic interests.
Take the case of Simpson’s paradox, which can be described as the situation in which conditional probabilities (often related to causal relations) are opposite for subpopulations than for the whole population. Let academic salaries be higher for economists than for sociologists, and let salaries within each group be higher for women than for men. But let there be twice as many men than women in economics and twice as many women than men in sociology. By construction, the average salary of women is higher than that for men in each group; yet, for the right values of the different salaries, women are paid less on average, taking both groups together. [Example: Economics — 2 men earn 100$, 1 woman 101$; Sociology — 1 man earn 90$, 2 women 91$. Average female earning: (101 + 2×91)/3 = 94.3; Average male earning: (2×100 + 90)/3 = 96.6 — LPS]
An aggregate model leads to the conclusion that that being female causes a lower salary. We might feel an uneasiness with such a model, since I have already filled in the details that show more precisely why the result comes about. The temptation is to say that the aggregate model shows that being female apparently causes lower salaries; but the more refined description of a disaggregated model shows that really being female causes higher salaries. A true paradox, however, is not a contradiction, but a seeming contradiction. Another way to look at it is to say that the aggregate model is really true at that level of aggregation and is useful for policy and that equally true more disaggregated model gives an explanation of the mechanism behind the true aggregate model.
It is not wrong to take an aggregate perspective and to say that being female causes a lower salary. We may not have access to the refined description. Even if we do, we may as matter of policy think (a) that the choice of field is not susceptible to useful policy intervention, and (b) that our goal is to equalize income by sex and not to enforce equality of rates of pay. That we may not believe the factual claim of (a) nor subscribe to the normative end of (b) is immaterial. The point is that that they mark out a perspective in which the aggregate model suits both our purposes and the facts: it tells the truth as seen from a particular perspective.