A Multiplicative Instrumental Variable Model for Data Missing Not-at-Random
非随机缺失数据的乘法工具变量模型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。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
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：摘要的核心是非随机缺失结果的识别与估计；工具变量和因果推断类比不足以证明其目标属于所列因果效应方向。
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
非随机缺失数据的乘法工具变量模型
- 研究问题
- 结果非随机缺失时，能否不预先限制结果尺度上的选择偏差程度，而借助工具变量和另一类选择机制限制实现识别？
- 方法与思路
- 作者要求工具变量预测不响应且与总体结果独立，并施加工具与隐藏共同因素在乘法尺度上无交互的选择模型。
- 材料陈述的贡献
- 摘要称，三项条件下可用单组 Wald 比率识别缺失结果的正则泛函，并据影响函数构造多重稳健估计器及工具变量扩展。
- 阅读关注点
- 应核对乘法无交互假设的含义、识别比率及必要分母条件、多重稳健性的模型组合，以及访员工具的有效性论证。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；不限制结果尺度偏差仍依赖选择模型，完整条件未说明。
原文摘要
Instrumental variable (IV) methods offer a valuable approach to account for outcome data missing not-at-random. A valid missing data instrument is a measured factor which (i) predicts the nonresponse process and (ii) is independent of the outcome in the underlying population. For point identification, all existing IV methods for missing data including the celebrated Heckman selection model, a priori restrict the extent of selection bias on the outcome scale, therefore potentially understating uncertainty due to missing data. In this work, we introduce an IV framework which allows the degree of selection bias on the outcome scale to remain completely unrestricted. The new approach instead relies for identification on (iii) a key multiplicative selection model, which posits that the instrument and any hidden common correlate of selection and the outcome, do not interact on the multiplicative scale. Interestingly, we establish that any regular statistical functional of the missing outcome is nonparametrically identified under (i)-(iii) via a single-arm Wald ratio estimand reminiscent of the standard Wald ratio estimand in causal inference. For estimation and inference, we characterize the influence function for any functional defined on a nonparametric model for the observed data, which we leverage to develop semiparametric multiply robust IV estimators. Several extensions of the methods are also considered, including the important practical setting of polytomous and continuous instruments. Simulation studies illustrate the favorable finite sample performance of proposed methods, which we further showcase in an HIV study nested within a household health survey study we conducted in Mochudi, Botswana, in which interviewer characteristics are used as instruments to correct for selection bias due to dependent nonresponse in the HIV component of the survey study.