When you have the right mental model, this is an appropriate approach.
Not every mental model is correct, either locally to a domain or globally in all cases.
1 < 2, in almost all cases except for small Z_{0,1} or similar type cases where mod functions modify the space to something exotic or comparator functions are defined far out of the typical. If you think there is a case for 2 < 1, and you aren't appealing to something exotic, you have the wrong approach.
Any mental model that assigns zero weight to the probability of being wrong is a wrong mental model.
That said, biases arising from endogeneity might have negative effects too. You can't conclude a parameter should have a different/zero sign just because of endogeneity, you have to go fix your model, and re-estimate the parameters.
> Any mental model that assigns zero weight to the probability of being wrong is a wrong mental model.
This is true when there is uncertainty. It also doesn't connect to "I only hire people who think like I do", which is the context of my response, so I don't follow your point.
Agreed on your other point that you need to instrument endogeneity. Another requirement is plausibility. Hookworm presence in Greenland should be uncorrelated to solar sunspots.
Not every mental model is correct, either locally to a domain or globally in all cases.
1 < 2, in almost all cases except for small Z_{0,1} or similar type cases where mod functions modify the space to something exotic or comparator functions are defined far out of the typical. If you think there is a case for 2 < 1, and you aren't appealing to something exotic, you have the wrong approach.