This paper presents a framework for causal inference in the presence of censored data, where the failure time is marked by a continuous variable known as a mark. The mark can be viewed as an extension of the failure cause in the classical competing risks model where the cause of failure is replaced by a continuous mark only observed at uncensored failure times. Due to the continuous nature of the marks, observations at each specific mark are sparse, making the identification and estimation of causality a challenging task. To address this issue, we define a new mark-specific treatment effect within the potential outcomes framework and characterize its identifying conditions. We then propose a local smoothing causal estimand and establish its asymptotic properties. We evaluate our method using simulation studies as well as a real dataset from the Antibody Mediated Prevention trials.

翻译：本文提出了一种在存在删失数据的情况下进行因果推断的框架，其中失效时间由一个被称为"标记"的连续变量所标记。该标记可视为经典竞争风险模型中失效原因的扩展，其中失效原因被一个仅在未删失效时间观测到的连续标记所取代。由于标记的连续性，每个特定标记处的观测数据稀疏，这使得因果关系的识别与估计成为一项具有挑战性的任务。为解决此问题，我们在潜在结果框架内定义了一种新的标记特异性处理效应，并刻画了其识别条件。随后，我们提出了一种局部平滑因果估计量，并建立了其渐近性质。我们通过模拟研究以及来自抗体介导预防试验的真实数据集对所提方法进行了评估。