The ability to generalize experimental results from randomized control trials (RCTs) across locations is crucial for informing policy decisions in targeted regions. Such generalization is often hindered by the lack of identifiability due to unmeasured effect modifiers that compromise direct transport of treatment effect estimates from one location to another. We build upon sensitivity analysis in observational studies and propose an optimization procedure that allows us to get bounds on the treatment effects in targeted regions. Furthermore, we construct more informative bounds by balancing on the moments of covariates. In simulation experiments, we show that the covariate balancing approach is promising in getting sharper identification intervals.

翻译：在各个地点推广随机控制试验(RCTs)的实验结果的能力对于为目标区域的政策决策提供信息至关重要,这种普遍化往往由于无法识别影响的变化因素而受阻,这些影响影响治疗效应估计数从一个地点直接迁移到另一个地点。我们在观察研究中的敏感性分析的基础上,提出了一个优化程序,使我们能够在目标区域获得治疗效果的界限。此外,我们通过平衡共变时间来构建更多的信息界限。在模拟实验中,我们表明共变平衡法在获得更清晰的识别间隔方面很有希望。