Design sensitivity and its implications for weighted observational studies
本文提出一个加权估计框架，利用设计敏感性指标帮助研究者在分析结果前评估不同设计选择对遗漏变量偏倚的稳健性，并应用于哥伦比亚和平协议支持度研究。
Abstract Careful design and preregistration of a treated-control comparison in an observational study enhances the quality of its evidence. However, sensitivity to unmeasured confounding is not typically a primary consideration in the preanalysis design stage. In the following paper, we introduce a framework for weighted estimators that allows researchers to optimize for robustness to omitted variable bias at the design stage using a measure called design sensitivity. Inspired by a similar measure for matching estimators, design sensitivity describes the asymptotic power of a sensitivity analysis, and allows researchers to transparently evaluate the impact of different estimation strategies on sensitivity to omitted confounders prior to outcome analysis. We apply this general framework to two commonly used sensitivity models, the marginal sensitivity model and the variance-based sensitivity model. By comparing design sensitivities, we interrogate how key features of weighted observational designs, including trimming weights, choosing between different treatment versions, and altering the study’s inclusion criteria, impact robustness to unmeasured confounding. We illustrate the proposed framework on a study examining drivers of support for the 2016 Colombian peace agreement.