论文标题
在因子结构结果的研究中对未观察到的混杂的敏感性
Sensitivity to Unobserved Confounding in Studies with Factor-structured Outcomes
论文作者
论文摘要
在这项工作中，我们提出了一种评估对具有多个结果的研究中未观察到的混杂感的敏感性的方法。我们展示了如何利用多结果环境独特的先验知识来增强因果结论，超出了从孤立分析个人结果所能获得的。我们认为，做出共同的混杂假设通常是合理的，在该假设下，结果之间的残留依赖性可用于简化和提高灵敏度分析。我们专注于一类因子模型，我们可以将所有结果的因果效应结合到一个有条件的因素效应上，该因素代表了未观察到的混杂因素解释的治疗方差的比例。我们表征了因果无知区域如何在对无效控制结果的其他先前假设下收缩，并提供了量化因果效应估计的鲁棒性的新方法。最后，我们在实践中说明了我们的敏感性分析工作流程，这是对模拟数据和案例研究的分析，其中包括来自国家健康和营养检查调查（NHANES）的数据。
In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-outcome setting can be leveraged to strengthen causal conclusions beyond what can be achieved from analyzing individual outcomes in isolation. We argue that it is often reasonable to make a shared confounding assumption, under which residual dependence amongst outcomes can be used to simplify and sharpen sensitivity analyses. We focus on a class of factor models for which we can bound the causal effects for all outcomes conditional on a single sensitivity parameter that represents the fraction of treatment variance explained by unobserved confounders. We characterize how causal ignorance regions shrink under additional prior assumptions about the presence of null control outcomes, and provide new approaches for quantifying the robustness of causal effect estimates. Finally, we illustrate our sensitivity analysis workflow in practice, in an analysis of both simulated data and a case study with data from the National Health and Nutrition Examination Survey (NHANES).
