Discovery and Inference of a Causal Network with Hidden Confounding
存在隐蔽混杂的因果网络发现与推断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。
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：摘要明确研究未观测混杂下的因果发现、工具变量、直接效应及因果检验，因此归入因果推断；因果图不足以单独支持网络干扰分类。
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…
存在隐蔽混杂的因果网络发现与推断
- 研究问题
- 高斯有向无环图存在未观测混杂时，如何发现祖先关系和候选工具变量，区分直接与中介效应，并检验多个因果效应？
- 方法与思路
- 作者提出GrIVET，将按顺序的因果发现、扩展剥离算法、分离中介效应的工具变量估计及考虑混杂的似然比检验结合。
- 材料陈述的贡献
- 摘要称方法具有对无效工具和不确定干预的稳健性、一致性、多项式复杂度及渐近有效推断，并展示模拟和基因表达应用。
- 阅读关注点
- 应核对隐藏混杂、工具变量与干预的具体条件，直接效应的识别方式，以及稳健性、复杂度和似然比检验结论的适用范围。
- 阅读边界
- 依据arXiv 2309.09420v1预印本摘要解读，不能据此声称已同行评审或期刊接收，也不能推断刊本变化；摘要未列各项保证的条件。
原文摘要
This article proposes a novel causal discovery and inference method called GrIVET for a Gaussian directed acyclic graph with unmeasured confounders. GrIVET consists of an order-based causal discovery method and a likelihood-based inferential procedure. For causal discovery, we generalize the existing peeling algorithm to estimate the ancestral relations and candidate instruments in the presence of hidden confounders. Based on this, we propose a new procedure for instrumental variable estimation of each direct effect by separating it from any mediation effects. For inference, we develop a new likelihood ratio test of multiple causal effects that is able to account for the unmeasured confounders. Theoretically, we prove that the proposed method has desirable guarantees, including robustness to invalid instruments and uncertain interventions, estimation consistency, low-order polynomial time complexity, and validity of asymptotic inference. Numerically, GrIVET performs well and compares favorably against state-of-the-art competitors. Furthermore, we demonstrate the utility and effectiveness of the proposed method through an application inferring regulatory pathways from Alzheimer's disease gene expression data.