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Nonparametric tests for treatment effect heterogeneity in observational studies
Author(s) -
Dai Maozhu,
Shen Weining,
Stern Hal S.
Publication year - 2023
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11728
Subject(s) - observational study , nonparametric statistics , propensity score matching , econometrics , statistics , statistic , parametric statistics , confounding , test statistic , normality , treatment effect , asymptotic distribution , mathematics , average treatment effect , statistical hypothesis testing , computer science , medicine , estimator , traditional medicine
We consider the problem of testing for treatment effect heterogeneity in observational studies and propose a nonparametric test based on multisampleU $$ U $$ ‐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 modelling 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 are discussed: an employment programme evaluation study and a mental health study of China's one‐child policy.