Proximal Causal Inference for Conditional Separable Effects
条件可分离效应的近端因果推断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：摘要直接研究未测混杂下条件可分离效应的近端识别与估计。
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…
条件可分离效应的近端因果推断
- 研究问题
- 当处理后事件与结果之间存在未测混杂时，如何识别并估计以该事件为条件的可分离效应，涵盖实验和观察研究？
- 方法与思路
- 作者利用隐藏共同原因的代理变量建立非参数识别，刻画半参数影响函数，并结合机器学习估计辅助函数与目标效应。
- 材料陈述的贡献
- 摘要称方法允许时变混杂，给出改进的辅助函数收敛率，并构造一致、渐近线性及局部半参数有效的估计量。
- 阅读关注点
- 核对处理可分离的含义、代理变量所需条件、桥函数求解及识别要求，关注影响函数和效率结论对应的统计模型。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未经核实。摘要未列出完整代理条件、收敛率和试验细节，代理变量存在本身不足以保证识别。
原文摘要
Scientists regularly pose questions about treatment effects on outcomes conditional on a post-treatment event. However, causal inference in such settings requires care, even in perfectly executed randomized experiments. Recently, the conditional separable effect (CSE) was proposed as an interventionist estimand that corresponds to scientifically meaningful questions in these settings. However, existing results for the CSE require no unmeasured confounding between the outcome and post-treatment event, an assumption frequently violated in practice. In this work, we address this concern by developing new identification and estimation results for the CSE that allow for unmeasured confounding. We establish nonparametric identification of the CSE in observational and experimental settings with time-varying confounders, provided that certain proxy variables for hidden common causes of the post-treatment event and outcome are available. For inference, we characterize an influence function for the CSE under a semiparametric model where nuisance functions are a priori unrestricted. Using modern machine learning methods, we construct nonparametric nuisance function estimators and establish convergence rates that improve upon existing results. Moreover, we develop a consistent, asymptotically linear, and locally semiparametric efficient estimator of the CSE. We illustrate our framework with simulation studies and a real-world cancer therapy trial.