Weighting methods are popular tools for estimating causal effects; assessing their robustness under unobserved confounding is important in practice. In the following paper, we introduce a new set of sensitivity models called "variance-based sensitivity models". Variance-based sensitivity models characterize the bias from omitting a confounder by bounding the distributional differences that arise in the weights from omitting a confounder, with several notable innovations over existing approaches. First, the variance-based sensitivity models can be parameterized with respect to a simple $R^2$ parameter that is both standardized and bounded. We introduce a formal benchmarking procedure that allows researchers to use observed covariates to reason about plausible parameter values in an interpretable and transparent way. Second, we show that researchers can estimate valid confidence intervals under a set of variance-based sensitivity models, and provide extensions for researchers to incorporate their substantive knowledge about the confounder to help tighten the intervals. Last, we highlight the connection between our proposed approach and existing sensitivity analyses, and demonstrate both, empirically and theoretically, that variance-based sensitivity models can provide improvements on both the stability and tightness of the estimated confidence intervals over existing methods. We illustrate our proposed approach on a study examining blood mercury levels using the National Health and Nutrition Examination Survey (NHANES).
翻译:首先,基于差异的敏感度模型可以在一个标准化和约束性的简单参数方面对基于差异的敏感度模型进行参数测量。我们采用了一种正式的基准化程序,使研究人员能够以可解释和透明的方式利用观察到的共变参数值来说明合理的参数值。 其次,我们表明,研究人员可以在一套基于差异的敏感度模型下估计有效的信任度间隔,并为研究人员提供扩展,以纳入他们关于分化器的实质性知识,从而帮助缩短间隔。最后,我们强调我们提议的方法和现有的敏感度分析之间的联系,同时从经验上和理论上证明基于差异的敏感度模型可以改进估计的国家卫生水平。</s>