Well, it certainly makes more sense to look at the by department data rather than the aggregate data. But I would say that the fact that we see such large disparities in which departments are applied to is already enough to refute the idea that male and female applicants are pretty much the same, and that therefore it would be pretty reckless to conclude any sex discrimination based on the difference in acceptance rates.
And Pearl's point is that the "real world" logic you just applied is both important and not statistical—you need to augment your analysis with these causal assumptions in order to translate probability into meaningful causal statements.
