在结果无关缺失下，给出因果效应的无假设界与敏感性分析方法。

Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding

• 基于结果无关缺失假设，推导因果效应的无前提边界。
• 提出敏感性分析工具，评估缺失机制对结论的影响。
• 适合处理缺失数据的因果推断研究者参考。

当混杂因素缺失不随机（MNAR）且缺失机制与结果无关时，本文给出了暴露与非暴露下潜在结果概率对比的无假设边界。同时，提出了一个敏感性分析方法，用于补充边界分析，评估缺失机制对因果效应估计的影响。该方法无需额外假设，适用于存在缺失数据的因果推断场景。

原文摘要 · Abstract (English)

We report assumption-free bounds for any contrast between the probabilities of the potential outcome under exposure and non-exposure when the confounders are missing not at random. We assume that the missingness mechanism is outcome-independent. We also report a sensitivity analysis method to complement our bounds.

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