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).
翻译:在这项工作中,我们提出一种方法,用于评估在多项结果的研究中未观察到的混乱的敏感性;我们展示如何利用多种结果背景下独特的先前知识,加强孤立分析个别结果所能实现的因果关系结论;我们主张,通常有理由作出共同的混乱假设,即结果之间的剩余依赖性可用来简化和强化敏感性分析;我们侧重于一组要素模型,我们可以将所有结果的因果关系以单一的敏感参数为条件,该参数代表未观察到的混淆者所解释的治疗差异的一小部分;我们说明因果无知区域如何在先前关于存在无效控制结果的假设的基础上萎缩,并提供新的方法量化因果关系估计数的稳健性;最后,我们在分析模拟数据和国家健康和营养调查(NHANES)数据时,说明我们在实践中的敏感性分析工作流程。