Calibrated sensitivity models
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究未测混杂敏感性分析及平均处理效应界的估计与推断。
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校准敏感性模型
- 研究问题
- 如何以已测混杂为基准解释未测混杂的敏感性参数，并把基准估计的不确定性纳入因果结论的稳健性评估？
- 方法与思路
- 作者用已测混杂的倍数直接约束未测混杂，为三类校准敏感性模型构造平均处理效应界的估计与推断方法。
- 材料陈述的贡献
- 摘要称建立清晰解释框架，并在双重稳健式非参数条件下得到参数效率与渐近正态性；纳入校准误差可改变稳健性判断。
- 阅读关注点
- 应核对已测混杂的度量、倍数约束的含义、三类模型差异，以及校准不确定性的传播和效率结论所需条件。
- 阅读边界
- 这是 arXiv 预印本，不能确认同行评审或期刊接收；摘要未列出完整条件，校准基准也不能验证真实未测混杂满足所设界。
原文摘要
In causal inference, sensitivity models assess how unmeasured confounders could alter causal analyses, but the sensitivity parameter -- which quantifies the degree of unmeasured confounding -- is often difficult to interpret. For this reason, researchers sometimes compare the sensitivity parameter to an estimate of measured confounding. This is known as calibration, or benchmarking. However, calibrated estimates are not always interpreted correctly, and uncertainty in the estimate of measured confounding is rarely accounted for. To address these limitations, we propose calibrated sensitivity models, which directly bound the degree of unmeasured confounding by a multiple of measured confounding. We develop a clear framework for interpreting calibrated sensitivity models and derive statistical methods for accounting for uncertainty due to estimating measured confounding. Incorporating this uncertainty shows causal analyses may be either less or more robust to unmeasured confounding than suggested by standard approaches. We develop efficient estimators and inferential methods for bounds on the average treatment effect with three calibrated sensitivity models, establishing parametric efficiency and asymptotic normality under doubly robust style nonparametric conditions. We illustrate our methods with an analysis of the effect of mothers' smoking on infant birthweight.