Inference on optimal treatment assignments

Timothy B. Armstrong () and Shu Shen ()
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Timothy B. Armstrong: University of Southern California
Shu Shen: University of California

The Japanese Economic Review, 2023, vol. 74, issue 4, No 3, 500 pages

Abstract: Abstract We consider inference on optimal treatment assignments. Our methods allow inference on the treatment assignment rule that would be optimal given knowledge of the population treatment effect in a general setting. The procedure uses multiple hypothesis testing methods to determine a subset of the population for which assignment to treatment can be determined to be optimal after conditioning on all available information, with a prespecified level of confidence. A Monte Carlo study confirms that the inference procedure has good small sample behavior. We apply the method to study Project STAR and the optimal assignment of a small class intervention based on school and teacher characteristics.

Keywords: Optimal treatment assignment; Set inference; Multiple testing (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s42973-023-00138-1

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