Survey weighting allows researchers to account for bias in survey samples, due to unit nonresponse or convenience sampling, using measured demographic covariates. Unfortunately, in practice, it is impossible to know whether the estimated survey weights are sufficient to alleviate concerns about bias due to unobserved confounders or incorrect functional forms used in weighting. In the following paper, we propose two sensitivity analyses for the exclusion of important covariates: (1) a sensitivity analysis for partially observed confounders (i.e., variables measured across the survey sample, but not the target population), and (2) a sensitivity analysis for fully unobserved confounders (i.e., variables not measured in either the survey or the target population). We provide graphical and numerical summaries of the potential bias that arises from such confounders, and introduce a benchmarking approach that allows researchers to quantitatively reason about the sensitivity of their results. We demonstrate our proposed sensitivity analyses using state-level 2020 U.S. Presidential Election polls.

翻译：调查加权允许研究人员利用测量到的人口统计协变量，来解决因单位无应答或便利抽样导致的调查样本偏差。然而，在实践中，我们无法确定估计的调查权重是否足以缓解对未观测混杂因素或加权中使用的错误函数形式所引起偏倚的担忧。在本文中，我们针对重要协变量的遗漏提出了两种敏感性分析：（1）针对部分观测混杂因素（即仅在调查样本中测量但未在目标总体中测量的变量）的敏感性分析，以及（2）针对完全未观测混杂因素（即既未在调查中测量也未在目标总体中测量的变量）的敏感性分析。我们提供了由这些混杂因素引起的潜在偏倚的图形和数值摘要，并引入了一种基准化方法，使研究人员能够定量地推理其结果的敏感性。我们使用2020年美国总统大选的州级民调数据来展示我们提出的敏感性分析方法。