Interactive identification of individuals with positive treatment effect while controlling false discoveries

Duan Boyan (), Wasserman Larry () and Ramdas Aaditya ()
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Duan Boyan: Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, 15213, United States of America, now at Google
Wasserman Larry: Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, 15213, United States of America, now at Google
Ramdas Aaditya: Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, 15213, United States of America, now at Google

Journal of Causal Inference, 2024, vol. 12, issue 1, 43

Abstract: Out of the participants in a randomized experiment with anticipated heterogeneous treatment effects, is it possible to identify which subjects have a positive treatment effect? While subgroup analysis has received attention, claims about individual participants are much more challenging. We frame the problem in terms of multiple hypothesis testing: each individual has a null hypothesis (stating that the potential outcomes are equal, for example), and we aim to identify those for whom the null is false (the treatment potential outcome stochastically dominates the control one, for example). We develop a novel algorithm that identifies such a subset, with nonasymptotic control of the false discovery rate (FDR). Our algorithm allows for interaction – a human data scientist (or a computer program) may adaptively guide the algorithm in a data-dependent manner to gain power. We show how to extend the methods to observational settings and achieve a type of doubly robust FDR control. We also propose several extensions: (a) relaxing the null to nonpositive effects, (b) moving from unpaired to paired samples, and (c) subgroup identification. We demonstrate via numerical experiments and theoretical analysis that the proposed method has valid FDR control in finite samples and reasonably high identification power.

Keywords: individual treatment effect; heterogeneous treatment effect; multiple testing; subgroup discovery (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:12:y:2024:i:1:p:43:n:1001

DOI: 10.1515/jci-2023-0059

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