  We consider the problem of testing for treatment effect heterogeneity in
observational studies, and propose a nonparametric test based on multisample
U-statistics. To account for potential confounders, we use reweighted data
where the weights are determined by estimated propensity scores. The proposed
method does not require any parametric assumptions on the outcomes and bypasses
the need for modeling the treatment effect for each study subgroup. We
establish the asymptotic normality for the test statistic, and demonstrate its
superior numerical performance over several competing approaches via simulation
studies. Two real data applications including an employment program evaluation
study and a mental health study of China's one-child policy are also discussed.
