Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing
针对逆概率加权法依赖不可检验的混淆变量假设的问题，提出一种更精确的敏感性分析方法，估计在给定未观测混淆程度下处理效应的最窄可能范围，改进了现有方法区间过宽的缺陷。
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 article 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, which we show gives bounds that are too wide even asymptotically. This analysis is based on new partial identification results for Tan’s marginal sensitivity model. Supplementary materials for this article are available online.