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, very few 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：从观察性研究中得出因果推论的基本挑战之一是，无未测量混杂的假设无法通过观测数据进行检验。因此，评估该假设被违反时的敏感性对于在观察性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已有多种敏感性分析框架，但很少有适用于多值治疗观察性研究的方法。为解决这一问题，我们提出了一种用于多值治疗设置下进行敏感性分析的框架。在该框架内，我们定义了一类广义的可加因果估计量，并证明在所提出的敏感性模型下，因果估计量的估计可以非常高效地实现。模拟结果表明，当协变量分布有足够重叠时，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液汞水平因果效应的观察性研究，展示了所提方法的应用。