Limited overlap between treated and control groups is a key challenge in observational analysis. Standard approaches like trimming importance weights can reduce variance but introduce a fundamental bias. We propose a sensitivity framework for contextualizing findings under limited overlap, where we assess how irregular the outcome function has to be in order for the main finding to be invalidated. Our approach is based on worst-case confidence bounds on the bias introduced by standard trimming practices, under explicit assumptions necessary to extrapolate counterfactual estimates from regions of overlap to those without. Empirically, we demonstrate how our sensitivity framework protects against spurious findings by quantifying uncertainty in regions with limited overlap.

翻译：处理组与对照组之间有限的重叠是观察性分析中的关键挑战。标准的处理方法（如修剪重要性权重）虽能降低方差，但会引入根本性偏差。我们提出了一种敏感性分析框架，用于在有限重叠情境下对研究结果进行语境化评估，通过分析结果函数需达到何种异常程度才能使主要结论失效。该方法基于对标准修剪操作所引入偏差的最坏情况置信界限，并依赖于将反事实估计从重叠区域外推至非重叠区域所需的显式假设。通过实证研究，我们展示了该敏感性框架如何通过量化有限重叠区域的不确定性，有效防范虚假研究结果。