Causal inference is an important tool in observational studies. Many estimation procedures have been developed under complete data and precise measurements. However, when the datasets contain the incomplete responses induced by right-censoring and the covariate subject to measurement error, little work has been available to simultaneously address these features. Moreover, prevalent sampling is also a frequent phenomenon in survival analysis, and it makes analysis challenging since prevalent sampling causes a biased sample. In this paper, we are interested in exploring the causal estimation with those complex features incorporated. We propose the valid estimation procedure to estimate the average causal effect and the survivor functions based on different treatment assignments. Theoretical results of the proposed method are also established. Numerical studies are reported to assess the performance of the proposed method.

中文翻译：

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具有协变量测量误差的左截断和右删截数据的因果推断

因果推理是观察研究中的重要工具。在完整的数据和精确的测量下已经开发出许多估计程序。但是，当数据集包含由右删失引起的不完全响应以及存在测量误差的协变量时，很少有工作可以同时解决这些特征。此外，流行采样也是生存分析中的常见现象，并且由于流行采样会导致样本有偏差，因此使分析具有挑战性。在本文中，我们有兴趣探索结合了这些复杂特征的因果估计。我们提出有效的估算程序，根据不同的治疗方案估算平均因果效应和幸存者功能。还建立了该方法的理论结果。