Semiparametric theory for causal mediation analysis: Efficiency bounds, multiple robustness and sensitivity analysis
因果中介分析的半参数理论：效率界、多重稳健性与敏感性分析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。
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：摘要明确研究自然直接与间接因果效应、混杂调整及识别假设的敏感性。
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…
因果中介分析的半参数理论：效率界、多重稳健性与敏感性分析
- 研究问题
- 在存在较多暴露前混杂因素时，如何推断边际自然直接与间接因果效应，并评估效率、模型稳健性及中介可忽略性假设的敏感性？
- 方法与思路
- 作者建立因果中介效应的半参数推断框架，提出多重稳健且局部有效的估计量，并构造针对中介可忽略性的双重稳健敏感性分析。
- 材料陈述的贡献
- 摘要称提供了自然直接与间接效应的新估计方法，推进中介分析中的效率与稳健性研究，并提出新的双重稳健敏感性分析框架。
- 阅读关注点
- 应核对自然效应的识别假设、多重稳健性允许哪些模型组合正确，以及局部效率条件和敏感性分析如何参数化中介不可忽略性。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；具体识别条件和稳健模型组合未列出，多重稳健性也不意味着可忽略任意混杂。
原文摘要
Whilst estimation of the marginal (total) causal effect of a point exposure on an outcome is arguably the most common objective of experimental and observational studies in the health and social sciences, in recent years, investigators have also become increasingly interested in mediation analysis. Specifically, upon evaluating the total effect of the exposure, investigators routinely wish to make inferences about the direct or indirect pathways of the effect of the exposure not through or through a mediator variable that occurs subsequently to the exposure and prior to the outcome. Although powerful semiparametric methodologies have been developed to analyze observational studies, that produce double robust and highly efficient estimates of the marginal total causal effect, similar methods for mediation analysis are currently lacking. Thus, this paper develops a general semiparametric framework for obtaining inferences about so-called marginal natural direct and indirect causal effects, while appropriately accounting for a large number of pre-exposure confounding factors for the exposure and the mediator variables. Our analytic framework is particularly appealing, because it gives new insights on issues of efficiency and robustness in the context of mediation analysis. In particular, we propose new multiply robust locally efficient estimators of the marginal natural indirect and direct causal effects, and develop a novel double robust sensitivity analysis framework for the assumption of ignorability of the mediator variable.