Probabilistic prediction for binary treatment choice: With focus on personalized medicine

Charles Manski

Journal of Econometrics, 2023, vol. 234, issue 2, 647-663

Abstract: This paper extends my research applying statistical decision theory to treatment choice with sample data, using maximum regret to evaluate the performance of statistical treatment rules. The specific new contribution is to study as-if optimization using estimates of illness probabilities in a class of medical decisions. Beyond its specifics, the paper sends a broad message. Statisticians and computer scientists have addressed conditional prediction for decision making in indirect ways, the former applying classical statistical theory and the latter measuring prediction accuracy in test samples. Neither approach is satisfactory. Statistical decision theory provides a coherent, generally applicable methodology.

Keywords: Statistical decision theory; Treatment choice; Maximum regret; As-if optimization (search for similar items in EconPapers)
JEL-codes: C44 I19 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Working Paper: Probabilistic Prediction for Binary Treatment Choice: with focus on personalized medicine (2021)
Working Paper: Probabilistic Prediction for Binary Treatment Choice: with Focus on Personalized Medicine (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:234:y:2023:i:2:p:647-663

DOI: 10.1016/j.jeconom.2022.07.009

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