A statistical test for the benefits of personalizing interventions.

Authors

Zhaoqi Li, Emma Brunskill

Published in

Science (New York, N.Y.). Volume 393. Issue 6807. Pages eaeb9506. Jul 09, 2026. Epub Jul 09, 2026.

Abstract

From medicine to marketing to social sciences, the promise of tailoring interventions to individuals is undeniable. However, practical applications force weighing personalization's potential benefits with its possible increased cost and fragility. We introduce a statistical hypothesis test that evaluates, given historical data, evidence that a personalized intervention policy's performance will surpass deploying the best single intervention. The test maintains strict Type I error control while achieving asymptotic normality with the minimal possible variance under specified conditions. Results on diverse datasets from job training, depression treatment, education, and recommendation systems demonstrate the test's versatility and its superior performance over alternatives. This test can support decision-makers throughout the intervention sciences by providing a simple and powerful quantification of the potential benefits of personalization.

PMID:
42424443
Bibliographic data and abstract were imported from PubMed on 10 Jul 2026.

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