Probabilistic Prediction for Binary Treatment Choice: with Focus on Personalized Medicine

Charles Manski

No 29358, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: This paper extends my research applying statistical decision theory to treatment choice with sample data, using maximum regret to evaluate the performance of treatment rules. The specific new contribution is to study as-if optimization using estimates of illness probabilities in clinical choice between surveillance and aggressive treatment. 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.

JEL-codes: C44 I19 (search for similar items in EconPapers)
Date: 2021-10
Note: EH TWP
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Published as Charles F. Manski, 2022. "Probabilistic prediction for binary treatment choice: With focus on personalized medicine," Journal of Econometrics, .

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Journal Article: Probabilistic prediction for binary treatment choice: With focus on personalized medicine (2023)
Working Paper: Probabilistic Prediction for Binary Treatment Choice: with focus on personalized medicine (2021)
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