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Abstract:
Existing medical guidelines are simple rules based on a small number of variables. With modern medical records, vastly more information is available on which treatment decisions could be based. We present evidence that incorporating more of this information could lead to extremely large health benefits. Our principle sample is 1.3 million patients: the universe of patients admitted to VA hospitals from 2000 to 2015 with measured Hemoglobin levels less than 10 (the range over which we observe transfusions in the data). We first show that treatment decisions are invariant to all patient characteristics except hemoglobin level – roughly 30% of patients receive transfusions regardless of age, past inpatient stays, and comorbities. Treatment effects estimated via OLS nonetheless vary substantially with observables. Using instruments based on regression discontinuity and quasi-random assignment of patients to physicians, we assess the degree of selection on unobservables. We find that physicians treat patients with unobservably larger benefits, but that most of the measured heterogeneity in “OLS” treatment effects reflects heterogeneity in underlying causal effects rather than selection. In a structural model combining random forest machine learning techniques with a model of selection, we find that that better targeting the existing number of transfusions would increase the average treatment effect of transfusions on mortality from 0.06 pp to 0.1 pp, saving 500 lives per year in our sample.