A Statistical Test for the Benefits of Personalizing Interventions
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 38 references that are in the map. First posted on arXiv 9 Jul 2026.
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12 of 38 references.
References · 12
- 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
- 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
- 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 254
- 2019Confidence Intervals for Policy Evaluation in Adaptive Experimentscited by 95
- 2018Offline Multi-Action Policy Learning: Generalization and Optimizationcited by 64
- 2020Inference for Batched Banditscited by 63
- 2021Post-Contextual-Bandit Inferencecited by 32
- 2021Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effectscited by 29
- 2018Cauchy combination test: a powerful test with analytic p-value calculation under arbitrary dependency structurescited by 20
- 2019Experimental Evaluation of Individualized Treatment Rulescited by 16
- 2022Instance-optimal PAC Algorithms for Contextual Banditscited by 16
- 2022Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experimentscited by 7
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Citing papers per year: 2026: 2.
2 papers.