Robust Sensitivity Analysis via Augmented Percentile Bootstrap under Simultaneous Violations of Unconfoundedness and Overlap
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无混杂与重叠性同时失效时基于增广百分位自助法的稳健敏感性分析
- 研究问题
- 观察性研究同时存在未观测混杂和缺乏重叠时，如何对重叠加权平均处理效应进行敏感性分析，并构造有效推断？
- 方法与思路
- 作者扩展边际敏感性模型，仅要求部分人群满足混杂界限，允许零或一的处理概率，以线性规划求界并采用增广百分位自助法。
- 材料陈述的贡献
- 摘要称框架无需结果有界，并提供计算高效的最坏情形界限；新自助法还处理涉及未观测变量的过度识别估计方程。
- 阅读关注点
- 应核对受界限约束的人群如何定义、重叠加权目标的识别与矩条件，以及线性规划和增广自助法的覆盖率条件。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未说明完整条件，无需结果有界不代表无需其他矩限制。
原文摘要
The identification of causal effects in observational studies typically relies on two standard assumptions: unconfoundedness and overlap. However, both assumptions are often questionable in practice: unconfoundedness is inherently untestable, and overlap may fail in the presence of extreme unmeasured confounding. While various approaches have been developed to address unmeasured confounding and extreme propensity scores separately, few methods accommodate simultaneous violations of both assumptions. In this paper, we propose a sensitivity analysis framework that relaxes both unconfoundedness and overlap, building upon the marginal sensitivity model. Specifically, we allow the bound on unmeasured confounding to hold for only a subset of the population, thereby accommodating heterogeneity in confounding and allowing treatment probabilities to be zero or one. Moreover, unlike prior work, our approach does not require bounded outcomes and focuses on overlap-weighted average treatment effects, which are both practically meaningful and robust to non-overlap. We develop computationally efficient methods to obtain worst-case bounds via linear programming, and introduce a novel augmented percentile bootstrap procedure for statistical inference. This bootstrap method handles parameters defined through over-identified estimating equations involving unobserved variables and may be of independent interest. Our work provides a unified and flexible framework for sensitivity analysis under violations of both unconfoundedness and overlap.