Evaluating the Impact of Treating the Optimal Subgroup

TL;DR

This paper develops a nonparametric method to identify and quantify the benefit of treating optimal subgroups within a population, addressing challenges in non-regular settings and cases with no treatment effect.

Contribution

It introduces a modified approach for estimating treatment effects in subgroups that is valid even when no overall treatment effect exists.

Findings

Method effectively estimates subgroup treatment impact.

Handles cases with no treatment effect.

Applicable in nonparametric models.

Abstract

Suppose we have a binary treatment used to influence an outcome. Given data from an observational or controlled study, we wish to determine whether or not there exists some subset of observed covariates in which the treatment is more effective than the standard practice of no treatment. Furthermore, we wish to quantify the improvement in population mean outcome that will be seen if this subgroup receives treatment and the rest of the population remains untreated. We show that this problem is surprisingly challenging given how often it is an (at least implicit) study objective. Blindly applying standard techniques fails to yield any apparent asymptotic results, while using existing techniques to confront the non-regularity does not necessarily help at distributions where there is no treatment effect. Here we describe an approach to estimate the impact of treating the subgroup which benefits from treatment that is valid in a nonparametric model and is able to deal with the case where there is no treatment effect. The approach is a slight modification of an approach that recently appeared in the individualized medicine literature.