Recent work has focused on the potential and pitfalls of causal identification in observational studies with multiple simultaneous treatments. Building on previous work, we show that even if the conditional distribution of unmeasured confounders given treatments were known exactly, the causal effects would not in general be identifiable, although they may be partially identified. Given these results, we propose a sensitivity analysis method for characterizing the effects of potential unmeasured confounding, tailored to the multiple treatment setting, that can be used to characterize a range of causal effects that are compatible with the observed data. Our method is based on a copula factorization of the joint distribution of outcomes, treatments, and confounders, and can be layered on top of arbitrary observed data models. We propose a practical implementation of this approach making use of the Gaussian copula, and establish conditions under which causal effects can be bounded. We also describe approaches for reasoning about effects, including calibrating sensitivity parameters, quantifying robustness of effect estimates, and selecting models that are most consistent with prior hypotheses.

翻译：近期研究聚焦于多重同步处理观测性研究中因果识别的潜力与陷阱。基于先前工作，我们证明即便已知给定处理下未测量混杂因子的条件分布，因果效应一般仍不可识别（尽管可能实现部分识别）。基于此结果，我们提出一种针对多重处理情境的敏感性分析方法，用于刻画潜在未观测混杂的影响效应，该方法可表征与观测数据兼容的因果效应范围。其核心思想是通过Copula分解结果变量、处理变量与混杂因子的联合分布，并可与任意观测数据模型叠加使用。我们提出基于高斯Copula的实用实施框架，建立了因果效应可被界定的条件，同时描述了效应推理的若干途径，包括校准敏感性参数、量化效应估计的稳健性，以及选择与先验假设最一致的模型。