Identification and estimation of causal effects in the presence of confounded principal strata
主分层存在混杂时的因果效应识别与估计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：摘要围绕主分层因果效应、未观测混杂及负对照识别与估计展开。
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
主分层存在混杂时的因果效应识别与估计
- 研究问题
- 当未观测变量同时混杂处理与主分层时，如何识别并估计由中间变量联合潜在值定义的主分层内因果效应？
- 方法与思路
- 作者利用一对负对照建立非参数识别，要求二者均不直接影响结果，再在半参数或参数模型中放松该要求并提出估计方法。
- 材料陈述的贡献
- 摘要称其建立了多种模型下的主分层因果效应识别结果，模拟表现良好，并以美国青年男性纵向调查数据作应用说明。
- 阅读关注点
- 应核对负对照的有效性与完整识别条件、放松直接效应限制所需的模型结构，以及主分层效应的估计步骤和不确定性处理。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核实；摘要未列出全部识别条件、估计性质或模拟数值，不能仅凭负对照数量判断可识别。
原文摘要
The principal stratification has become a popular tool to address a broad class of causal inference questions, particularly in dealing with non-compliance and truncation-by-death problems. The causal effects within principal strata which are determined by joint potential values of the intermediate variable, also known as the principal causal effects, are often of interest in these studies. Analyses of principal causal effects from observed data in the literature mostly rely on ignorability of the treatment assignment, which requires practitioners to accurately measure as many as covariates so that all possible confounding sources are captured. However, collecting all potential confounders in observational studies is often difficult and costly, the ignorability assumption may thus be questionable. In this paper, by leveraging available negative controls that have been increasingly used to deal with uncontrolled confounding, we consider identification and estimation of causal effects when the treatment and principal strata are confounded by unobserved variables. Specifically, we show that the principal causal effects can be nonparametrically identified by invoking a pair of negative controls that are both required not to directly affect the outcome. We then relax this assumption and establish identification of principal causal effects under various semiparametric or parametric models. We also propose an estimation method of principal causal effects. Extensive simulation studies show good performance of the proposed approach and a real data application from the National Longitudinal Survey of Young Men is used for illustration.