Inverse propensity weighting (IPW) is a popular method for estimating treatment effects from observational data. However, its correctness relies on the untestable (and frequently implausible) assumption that all confounders have been measured. This paper introduces a robust sensitivity analysis for IPW that estimates the range of treatment effects compatible with a given amount of unobserved confounding. The estimated range converges to the narrowest possible interval (under the given assumptions) that must contain the true treatment effect. Our proposal is a refinement of the influential sensitivity analysis by Zhao, Small, and Bhattacharya (2019), which we show gives bounds that are too wide even asymptotically. This analysis is based on new partial identification results for Tan (2006)'s marginal sensitivity model.

翻译：逆概率加权（IPW）是一种从观测数据中估计处理效应的常用方法。然而，其正确性依赖于所有混杂变量已被测量的假设，这一假设不仅无法验证，且往往不切实际。本文提出一种针对IPW的稳健敏感性分析方法，该方法能估计与给定未观测混杂程度兼容的处理效应范围。所估计的范围收敛于必须包含真实处理效应的最窄可能区间（在给定假设下）。我们的方法是Zhao、Small与Bhattacharya (2019)提出的具有影响力的敏感性分析的改进版本，我们证明该方法给出的界限即使在渐近意义上也过宽。本分析基于Tan (2006)边际敏感性模型的新部分可识别结果。