I always have mixed feelings about instrumental variables (at least insofar as the instrument is not randomization). On one hand they show amazing promise as a way to handle unmeasured confounding. On the other hand, it is difficult to know if the assumptions required for a variable to be an instrument are being met or not.
This is a important dilemma. Alan Brookhart, who introduced them into phamracoepidemiology in 2006, has done an amazing job of proving out one example. But you can't generalize from one example and the general idea of using physician preference as an instrument, while really cool, suffers from these assumptions.
Unlike unmeasured confounders, it's hard to know how to test this. With unmeasured confounders you can ask critics to specify what they suspect might be the key confounding factors and go forth and measure them. But instruments are used precisely when there is a lack of data.
I've done some work in the area with some amazing colleagues and I still think that the idea has some real promise. It's a novel idea that really came out of left field and has enormous potential. But I want to understand it in far more actual cases before I conclude much more . . .
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