2025/06/13 by Kosuke Morikawa, Morikawa, Kosuke, Sho Komukai +3
Computer Science · Mathematics · #FOS: Computer and information sciences #Methodology (stat.ME) #Neural Networks and Applications #stat.ME
paper · pdf · doi:10.48550/arxiv.2506.11482
openalex publication_date 2025/06/13 · openalex created_date 2025/10/11 · arxiv created 2026/08/03 · arxiv updated 2026/08/04 · openalex updated_date 2026/08/04
Combining an internal individual-level study with readily available external summary statistics promises major efficiency gains at minimal additional cost, yet heterogeneity between sources can bias estimates for the internal target population. We develop a generalized entropy-balancing integration strategy that calibrates external moments to the internal covariate distribution, explicitly permitting a biased external sample. Our estimator of the internal-population mean is doubly robust: it remains consistent when either the outcome-regression model or the entropy-balancing modelis correctly specified. When multiple balancing specifications are plausible, we introduce a data-adaptive selection rule. We also provide easy-to-compute, fully estimable diagnostics-based on the Mahalanobis distance and the Pearson chi-square divergence-that pinpoint when integration is guaranteed to strictly outperform the internal sample mean. The approach is implemented in the R package daisy. Simulations and an application to nationwide public-access defibrillation records in Japan demonstrate meaningful precision gains while maintaining bias control under distributional shift.