Sensitivity analysis informs causal inference by assessing the sensitivity of conclusions to departures from assumptions. The consistency assumption states that there are no hidden versions of treatment and that the outcome arising naturally equals the outcome arising from intervention. When reasoning about the possibility of consistency violations, it can be helpful to distinguish between covariates and versions of treatment. In the context of surgery, for example, genomic variables are covariates and the skill of a particular surgeon is a version of treatment. There may be hidden versions of treatment, and this paper addresses that concern with a new kind of sensitivity analysis. Whereas many methods for sensitivity analysis are focused on confounding by unmeasured covariates, the methodology of this paper is focused on confounding by hidden versions of treatment. In this paper, new mathematical notation is introduced to support the novel method, and example applications are described.

翻译：敏感性分析通过评估结论对假设偏离的敏感程度来指导因果推断。一致性假设指出不存在隐藏的治疗版本，且自然产生的结局等于干预产生的结局。在考虑一致性假设可能被违反时，区分协变量与治疗版本是有益的。以外科手术为例，基因组变量属于协变量，而特定外科医生的技术则属于治疗版本。可能存在隐藏的治疗版本，本文通过一种新型敏感性分析方法来应对这一问题。尽管许多敏感性分析方法侧重于未测量协变量引起的混杂，本文方法则聚焦于隐藏治疗版本导致的混杂。本文引入了新的数学符号以支持这一创新方法，并描述了示例应用。