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重叠是观察性分析中的关键挑战。标准方法如对重要性权重进行修剪可降低方差，但会引入根本性偏差。我们提出一种敏感性框架，用于在有限重叠条件下情境化研究发现，通过评估结果函数需达到何种不规则程度才能使主要结论失效。该框架基于在明确假设下对标准修剪实践所引入偏差的最坏情况置信界，这些假设对于从重叠区域外推反事实估计至非重叠区域是必要的。在实证中，我们展示了敏感性框架如何通过量化有限重叠区域的不确定性，有效防范虚假发现。