Certainly to an extent we would. If we looked in the data above and noticed a dramatic difference between African and Middle Eastern based terrorist data, we may want to add that variable to our model such that it considers (size, age, location). Data modelling techniques are generally useful. Random Decision Forests and that sort of thing. Humans are pretty good at generating hypothesis from sparse data because they have good 'common sense' understanding of the causality structure of the world.

I wouldn't claim that we'll have a particularly accurate result, but the above strikes me as the kind of conclusion that one might be certain about because of it's mathiness, and yet because reality is nonlinear, any extra considerations beyond two variables might swing the results around wildly.

Well, we have theories about how to work with it. But the study of terrorism has one of the highest words:applications ratios I've ever heard of, and unambiguous successes seem thin on the ground despite a large volume of theory. Of course, it's also possible that the limits of information availability are distorting my picture of the field.

The US Army's FM 3-24 on counterinsurgency operations might be the best summary of the mainstream perspective (whatever that means in this context) that I've read, adjusting for its authorship, age, and goals.