Machine Predictions and Human Decisions with Variation in Payoffs and Skill

Michael Allan Ribers and Hannes Ullrich

No 8702, CESifo Working Paper Series from CESifo

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.

Keywords: prediction policy; expert decision-making; machine learning; antibiotic prescribing (search for similar items in EconPapers)
JEL-codes: C10 C55 I11 I18 Q28 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-big
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Citations: View citations in EconPapers (5)

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Related works:
Working Paper: Machine Predictions and Human Decisions with Variation in Payoffs and Skill (2020)
Working Paper: Machine Predictions and Human Decisions with Variation in Payoffs and Skills (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_8702

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