What does it mean to make an identification assumption? Trying to understand this problem:


  Suppose you had access to a dataset that follows individuals from
  adolescence throughout adulthood. Each year, you observe earnings,
  educational attainment, health, and marital status.  Suppose you were
  interested in seeing how marriage causally affects the earnings of
  men. Using the described data, how would you examine this question?  What
  would be your identifying assumption?  How could you assess the
  plausibility of this assumption?


First I'm trying to understand, what is an identifying assumption? I think it's the assumption you test that tells you whether there was a causal relationship or not. Is that right?

And my first thoughts on answering the homework problem are these:

I would have to determine that income and marriage aren't simultaneously determined, reverse causal, or both influenced by the same variable. So creating a counterfactual group (unmarried men) and matching with married men could set up a good difference in differences.

But how to assess the plausibility of this assumption? I usually just plug numbers into the computer and don't care about plausibility. What makes an assumption more plausible in econometrics?
Identifying assumption: assumptions made about the DGP that allows you to draw causal inference. E.g. exogeneity assumption for IV, parallel trends assumption in diff-in-diff.

Identifying assumptions (lack of endogeneity in general) can never be
statistically confirmed (a non-reject is good, but it's not confirmation). So assessment of plausibility consists of empirical arguments based on what you know about the DGP.