Longitudinal causal inference under informative missingness using the self-censoring model
自删失模型下信息性缺失的纵向因果推断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：摘要明确围绕纵向治疗策略效应的识别与估计，并处理信息性缺失和混杂控制。
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
自删失模型下信息性缺失的纵向因果推断
- 研究问题
- 当纵向协变量和结局的观测概率依赖其未观测值时，如何识别并估计仅在测量时点能够修改的治疗策略效应？
- 方法与思路
- 作者结合自删失模型、影子变量条件矩与递归因果识别，推导导数梯度及二阶余项，并以交叉拟合支持机器学习估计。
- 材料陈述的贡献
- 摘要称，该方法覆盖两种混杂控制情形，在适当乘积速率条件下实现渐近线性和根号样本量推断，包括跨结局时点的同时推断。
- 阅读关注点
- 应核对自删失与影子变量限制、未测量混杂变量的处理、递归识别条件，以及辅助函数学习和同时推断的速率要求。
- 阅读边界
- 这是arXiv预印本，同行评审或期刊接收未获核验。摘要未列出完整识别及乘积速率条件，也未提供实际数据中的推断表现。
原文摘要
Longitudinal observational data are often subject to informative missingness. For example, in healthcare research using electronic health records, covariates and outcomes are measured only when patients interact with the healthcare system. Whether these data are observed usually depends on the unobserved variable values themselves, introducing a common form of data missing not at random. We develop identification and estimation methods for the effects of longitudinal modified treatment policies under a self-censoring model which explicitly models this dependence. We consider effects of treatments that can be modified only at measurement times (e.g., interactions with the heathcare system). We study two settings: one in which the observed history suffices for treatment confounding control, and another in which confounder control requires covariate values that were not measured at missed visits. In both settings, we combine longitudinal causal identification with shadow-variable conditional moment restrictions to obtain recursive identification formulas for the counterfactual outcome mean. We derive gradients of the pathwise derivatives and second-order remainder expansions, and use these results to construct estimators that accommodate machine learning for nuisance-function estimation. Under suitable product-rate conditions and cross-fitting, the estimators are asymptotically linear and permit root-n inference, including simultaneous inference across outcome times. We also describe conditional-moment learning and ensemble approaches for estimating the nuisance functions required by our approach, together with a pool-and-smooth strategy that borrows information across sparsely measured outcomes.