The synthetic instrument: from sparse association to sparse causation
合成工具变量：从稀疏关联到稀疏因果效应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多暴露因果效应的识别与估计。
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…
关注这篇论文的作者
合成工具变量：从稀疏关联到稀疏因果效应
- 研究问题
- 在多个暴露影响单一结果、且存在未观测混杂的观察性研究中，能否利用因果效应的稀疏性实现识别和估计？
- 方法与思路
- 作者从观测暴露构造合成工具变量，在稀疏因果设定下将因果效应估计转化为零范数惩罚问题，并使用现成软件求解。
- 材料陈述的贡献
- 摘要称，稀疏因果设定下可在未观测混杂存在时识别效应；所述方法在低维和高维模拟中优于比较方法，并有小鼠肥胖数据应用。
- 阅读关注点
- 应核对稀疏性以外的完整识别条件、合成工具变量的构造与有效性，以及惩罚选择、求解保证和稀疏假设失效时的表现。
- 阅读边界
- 仅据所给 Crossref 摘要；未说明完整模型及识别条件，不能理解为稀疏性单独足以处理任意混杂，模拟优势也未获独立验证。
原文摘要
Abstract In many observational studies, researchers are often interested in the effects of multiple exposures on a single outcome. Standard approaches for high-dimensional data, such as the Lasso, assume that the associations between the exposures and the outcome are sparse. However, these methods do not estimate causal effects in the presence of unmeasured confounding. In this paper, we consider an alternative approach that assumes the causal effects under consideration are sparse. We show that under sparse causation, causal effects are identifiable even with unmeasured confounding. Our proposal is built around a novel device called the synthetic instrument, which, in contrast to standard instrumental variables, can be constructed directly from the observed exposures. We demonstrate that, under the assumption of sparse causation, the problem of causal effect estimation can be formulated as an ℓ0-penalization problem and solved efficiently using off-the-shelf software. Simulations show that our approach outperforms state-of-the-art methods in both low- and high-dimensional settings. We further illustrate our method using a mouse obesity dataset.