A0833
Title: Nonparametric tests of treatment effect homogeneity for policy-makers
Authors: Oliver Dukes - Ghent University (Belgium)
Mats Stensrud - Ecole polytechnique federale de Lausanne (Switzerland)
Aaron Hudson - Fred Hutchinson Cancer Center (United States) [presenting]
Riccardo Brioschi - Ecole polytechnique federale de Lausanne (Switzerland)
Abstract: The focus is on nonparametric estimation of conditional treatment effects, but inference has remained relatively unexplored. A class of nonparametric tests are proposed for both quantitative and qualitative treatment effect heterogeneity. The tests can incorporate a variety of structured assumptions on the conditional average treatment effect, allow for both continuous and discrete covariates, and do not require sample splitting. Furthermore, it is shown how the tests are tailored to detect alternatives where the population impact of adopting a personalized decision rule differs from using a rule that discards covariates. The proposal is thus relevant for guiding treatment policies. The utility of the proposal is borne out in simulation studies and a re-analysis of an AIDS clinical trial.
