Confidence Interval Construction for Causally Generalized Estimates With Target Sample Summary Information

Generalizing causal findings, such as the average treatment effect (ATE), from a source to a target population is a critical topic in biomedical research. Differences in the distributions of treatment effect modifiers between these populations, known as covariate shift, can lead to varying ATEs. Chen et al. [1] introduced a weighting method to estimate the target ATE using only summary-level information from a target sample while accounting for the possible covariate shifts. However, the asymptotic variance of the estimate was shown to depend on individual-level data from the target sample, hindering statistical inference. In this article, we propose a resampling-based perturbation method for confidence interval construction for the estimated target ATE, utilizing additional summary-level information. We demonstrate the effectiveness of our approach through simulation and real data settings when only summary-level information is available.