Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing
Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing
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DOI：
10.1080/01621459.2022.2069572
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发表时间：
2022-05-28
影响因子：
3.7
通讯作者：
Guo, Kevin
中科院分区：
文献类型：
--
作者：
Dorn, Jacob;Guo, Kevin
逆倾向加权（IPW）是一种根据观察数据估计治疗效果的流行方法。然而，它的正确性依赖于无法测试的（而且常常令人难以置信的）假设，即所有混杂因素都已被测量。本文介绍了 IPW 的稳健敏感性分析，该分析估计与给定数量的未观察到的混杂因素相一致的治疗效果范围。估计范围收敛到必须包含真实治疗效果的最窄可能区间（在给定假设下）。我们的建议是对赵、斯莫尔和巴塔查里亚有影响力的敏感性分析的改进，我们证明它给出的界限甚至是渐近的都太宽了。该分析基于 Tan 边际敏感性模型的新的部分识别结果。本文可在线获取。
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.