The purpose of this essay is to discuss the "human capital" approach to inference. Observed decisions by experts can be used to organize data on their decisions using simple machine learning techniques. The fact that the human capital of these experts is heterogeneous implies that errors in decision making are inevitable, which in turn allows us to identify the conditional average treatment effect for a wider class of situations than would be possible with randomized control trials. This point is illustrated with some data from medical decision making in the context of treating depression, heart disease, and adverse childbirth events.
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