  In modern drug development, the broader availability of high-dimensional
observational data provides opportunities for scientist to explore subgroup
heterogeneity, especially when randomized clinical trials are unavailable due
to cost and ethical constraints. However, a common practice that naively
searches the subgroup with a high treatment level is often misleading due to
the "subgroup selection bias." More importantly, the nature of high-dimensional
observational data has further exacerbated the challenge of accurately
estimating the subgroup treatment effects. To resolve these issues, we provide
new inferential tools based on resampling to assess the replicability of
post-hoc identified subgroups from observational studies. Through careful
theoretical justification and extensive simulations, we show that our proposed
approach delivers asymptotically sharp confidence intervals and debiased
estimates for the selected subgroup treatment effects in the presence of
high-dimensional covariates. We further demonstrate the merit of the proposed
methods by analyzing the UK Biobank data.
