An average-case sensitivity analysis for unmeasured confounding
未观测混杂的平均强度敏感性分析AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要围绕无混杂假设、未观测混杂、平均潜在结果界及敏感性推断展开。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
未观测混杂的平均强度敏感性分析
- 研究问题
- 如何以未观测混杂的平均强度而非最坏情形上限刻画无混杂假设的偏离，并得到平均潜在结果的敏感性界？
- 方法与思路
- 作者以倾向得分比值的二阶矩参数化敏感性模型，通过优化推导尖锐界，并用有效影响函数及乘子自助法进行推断。
- 材料陈述的贡献
- 摘要称，得到平均潜在结果的闭式尖锐界、高效一步估计量和敏感性曲线的同时置信带，并展示协变量校准应用。
- 阅读关注点
- 应核对倾向得分比值的具体定义、尖锐界的可达性条件、敏感性参数校准方式，以及同时置信带的有效性要求。
- 阅读边界
- 仅据所给 Crossref 摘要，不能认定平均强度模型在所有场景都给出更紧界；不同模型限制下的比较需查全文。
原文摘要
Summary Sensitivity analysis for the unconfoundedness assumption is crucial in observational studies. For this purpose, the marginal sensitivity model has gained popularity in recent years owing to its good interpretability and mathematical properties. However, most existing models only consider a worst-case parameter that bounds the logit difference between the observed-data and full-data propensity scores, which may not fully capture the extent of unmeasured confounding. We propose a new sensitivity model that is parameterized by the second moment of the propensity score ratio, requiring only the average strength of unmeasured confounding to be bounded. By characterizing the associated sensitivity analysis as an optimization problem, we derive sharp closed-form bounds on the average potential outcomes under our model. We propose efficient one-step estimators for these bounds based on the corresponding efficient influence functions. Additionally, we use the multiplier bootstrap to construct simultaneous confidence bands to cover the sensitivity curve, consisting of bounds at different values of the sensitivity parameters. Through a real-data study, we illustrate how this average-case sensitivity analysis can provide tighter bounds and facilitate calibration of the results using observed covariates.