Interactive identification of individuals with positive treatment effect while controlling false discoveries
arxiv(2021)
摘要
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.
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