One of the fundamental challenges in drawing causal inferences from observational studies is that the assumption of no unmeasured confounding is not testable from observed data. Therefore, assessing sensitivity to this assumption's violation is important to obtain valid causal conclusions in observational studies. Although several sensitivity analysis frameworks are available in the casual inference literature, none of them are applicable to observational studies with multivalued treatments. To address this issue, we propose a sensitivity analysis framework for performing sensitivity analysis in multivalued treatment settings. Within this framework, a general class of additive causal estimands has been proposed. We demonstrate that the estimation of the causal estimands under the proposed sensitivity model can be performed very efficiently. Simulation results show that the proposed framework performs well in terms of bias of the point estimates and coverage of the confidence intervals when there is sufficient overlap in the covariate distributions. We illustrate the application of our proposed method by conducting an observational study that estimates the causal effect of fish consumption on blood mercury levels.

翻译：从观测研究中得出因果推断的基本挑战之一是，无法通过观测数据检验无未测量混杂假设。因此，评估该假设被违反时的敏感性对于在观测研究中获得有效的因果结论至关重要。尽管因果推断文献中存在多种敏感性分析框架，但它们均不适用于多值处理观测研究。为解决这一问题，我们提出了一种适用于多值处理设置的敏感性分析框架。在该框架内，我们提出了一类通用的加性因果估计量。我们证明，在提议的敏感性模型下，因果估计量的估计可以非常高效地执行。模拟结果表明，当协变量分布存在充分重叠时，所提框架在点估计偏差和置信区间覆盖率方面表现良好。我们通过一项评估鱼类消费对血汞水平因果效应的观测研究，展示了所提方法的应用。