Semiparametric Localized Principal Stratification Analysis with Continuous Strata
连续主分层的半参数局部化分析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。
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：摘要明确研究处理后中间变量定义的主分层因果效应及其识别和推断。
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
连续主分层的半参数局部化分析
- 研究问题
- 连续处理后中间变量产生无限多个主分层，使主分层因果效应难以识别且非正则；如何构造可估计并可推断的目标？
- 方法与思路
- 作者用 copula 主分层得分模型结合弱主分层可忽略性识别效应，再以局部泛函替代原目标，并利用其预言机情形影响函数构造估计。
- 材料陈述的贡献
- 摘要称估计器具有双重稳健性和渐近正态性；带宽趋零时达到非参数极小极大最优性，固定带宽时对局部目标达到半参数效率。
- 阅读关注点
- 应核对 copula 与可忽略性假设、预言机影响函数简化的代价，以及两种带宽下估计目标、偏差和效率结论的区别。
- 阅读边界
- 这是 arXiv 预印本，同行评审和期刊接收未经核实。摘要未列出正则条件及带宽速率，不能将局部目标的效率直接等同于原效应效率。
原文摘要
Principal stratification is essential for revealing causal mechanisms involving post-treatment intermediate variables, in real-world applications like surrogate marker evaluation. Principal stratification analysis with continuous intermediate variables is increasingly common but challenging due to the infinite principal strata and the nonidentifiability and nonregularity of principal causal effects. Inspired by recent research, we resolve these challenges by first using a flexible copula-based principal score model to identify principal causal effect under weak principal ignorability. We then target the local functional substitute of principal causal effect, which is statistically regular and can accurately approximate principal causal effect with vanishing bandwidth. We simplify the full efficient influence function of the local functional substitute by considering its oracle-scenario alternative. This leads to a computationally efficient and straightforward estimator for the local functional substitute and principal causal effect with vanishing bandwidth. We prove the double robustness of our proposed estimator, and derive its asymptotic normality for inferential purposes. With a vanishing bandwidth, our method attains minimax optimality for the nonparametric estimation of the principal causal effect. With a fixed bandwidth, it achieves semiparametric efficiency in estimating its local functional substitute. We demonstrate the strong performance of our proposed estimator through simulations and apply it to surrogate analysis of short-term CD4 count in ACTG 175.