Also Vietnam vets and LSD-fueled hippies exist in the same set, and each can have effects that go in different directions. On top of that, merging their effect

Also Vietnam vets and LSD-fueled hippies exist in the same set, and each can have effects that go in different directions. On top of that, merging their effects (in policy setting for example) sort of is a barrier to attributing causality. It forces a 2 dimensional signal to go to 1 dimension. Neural networks can backpropagate attribution through that, but only with the convention that this attribution is the marginal effect. And that's a complex problem: who would be responsible for a policy change when there's a population contributing long-term for 48%, but a short term population adds 4%. Which population is the real cause? the 48% or the 4%? Is there a right answer to this question?