Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/229520
Authors: 
Ribers, Michael Allan
Ullrich, Hannes
Year of Publication: 
2020
Series/Report no.: 
CESifo Working Paper No. 8702
Abstract: 
Human decision-making differs due to variation in both incentives and available information. This generates substantial challenges for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on large-scale administrative data into a choice model featuring heterogeneity in decision maker payoff functions and predictive skill. We apply our framework to the major health policy problem of improving the efficiency in antibiotic prescribing in primary care, one of the leading causes of antibiotic resistance. Our analysis reveals large variation in physicians’ skill to diagnose bacterial infections and in how physicians trade off the externality inherent in antibiotic use against its curative benefit. Counterfactual policy simulations show the combination of machine learning predictions with physician diagnostic skill achieves a 25.4 percent reduction in prescribing and the largest welfare gains compared to alternative policies for estimated as well as plausible hypothetical weights on the antibiotic resistance externality.
Subjects: 
prediction policy
expert decision-making
machine learning
antibiotic prescribing
JEL: 
C10
C55
I11
I18
Q28
Document Type: 
Working Paper

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