We propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. Under a shared confounding assumption, we argue that it is often reasonable to use residual dependence amongst outcomes to infer a proxy distribution for unobserved confounders. 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 further characterize how causal ignorance regions shrink under assumptions about null control outcomes, propose strategies for benchmarking sensitivity parameters, and derive metrics for quantifying the robustness of effect estimates. Finally, we propose a Bayesian inference strategy for quantifying uncertainty and describe a practical sensitivity workflow which we demonstrate in both simulation and in a case study using data from the National Health and Nutrition Examination Survey (NHANES).

翻译：我们提出一种方法来评估在具有多重结果的研究中未观察到的混乱现象的敏感性。在一个共同的令人困惑的假设下,我们争辩说,使用结果之间的剩余依赖性来推断未观察到的困惑者的代理分布往往是合理的。我们侧重于一组因素模型,我们可以将所有结果的因果关系以单一的敏感性参数为条件,该参数代表未观察到的困惑者所解释的治疗差异的一小部分。我们进一步说明因果无知地区如何根据无效控制结果的假设而萎缩,提出确定敏感度参数基准的战略,并得出量化可靠效果估计数的衡量标准。 最后,我们提出一个用于量化不确定性的巴耶斯推论战略,并描述我们利用国家健康和营养调查(NHANES)的数据在模拟和案例研究中展示的实际敏感性工作流程。