Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing

• 成果类型: Article
• 署名作者: Dorn, Jacob; Guo, Kevin
• 署名单位: Princeton University; Stanford University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2022.2069572
• 发表日期: 2023
• 页码: 2645-2657
• 关键词: design sensitivity; models; identification; probabilities; bounds; wages

摘要

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. for this article are available online.