[t_mann]

Every statistical model makes assumptions. As a general rule, the more mathematically complex the model, the fewer (or weaker) assumptions are made. That's what the complexity is for. So the criticism 'it looks complex, so the assumptions are probably weird' doesn't make sense.

If as a reader you don't understand a paper (that's been reviewed by experts), then the best thing to conclude is that you're not the target audience, not that the findings can be dismissed.

[roenxi]

He isn't saying that, he's saying he does understand the paper and therefore the findings can be viewed with some suspicion. That is the nature of research, clear conclusions are rare because real data is messy.

> Every statistical model makes assumptions. As a general rule, the more mathematically complex the model, the fewer (or weaker) assumptions are made. That's what the complexity is for. So the criticism 'it looks complex, so the assumptions are probably weird' doesn't make sense.

This is an argument of the form [X -> Y. Y. Y has a purpose. Therefore not(Y->Z)]. It isn't valid; the fact that a criticism is general doesn't make it weaker (or stronger, for that matter). It is a bit like saying meat contains bacteria so someone can't complain that some meal gave them food poisoning. They can certainly complain about it and it is possible (indeed likely) that some meat is bad because of excessive bacteria.

[t_mann]

> He isn't saying that, he's saying he does understand the paper

He literally says 'I can't really follow what it's doing', linking to a paper that discusses some issues with instrumental variable regression (what GMM is used for).