并行成果的承诺

IF 2.4 2区 数学 Q2 BIOLOGY
Biometrika Pub Date : 2024-02-17 DOI:10.1093/biomet/asae008
Ying Zhou, Dingke Tang, Dehan Kong, Linbo Wang
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引用次数: 0

摘要

从观察性研究中进行因果推断的一个主要挑战是在存在未测量混杂因素的情况下识别和估计因果效应。在本文中,我们介绍了一种新的因果推断方法,该方法利用多个结果的信息来处理无法测量的混杂因素。我们方法的一个重要假设是多个结果之间的条件独立性。与现有文献中的建议不同,条件独立性假设中多个结果的角色是对称的,因此被称为平行结果。我们展示了至少三个平行结果的非参数可识别性,并提供了一组线性结构方程模型下的参数估计工具。我们通过一组合成数据和真实数据分析对我们的建议进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Promises of Parallel Outcomes
A key challenge in causal inference from observational studies is the identification and estimation of causal effects in the presence of unmeasured confounding. In this paper, we introduce a novel approach for causal inference that leverages information in multiple outcomes to deal with unmeasured confounding. An important assumption in our approach is conditional independence among multiple outcomes. In contrast to existing proposals in the literature, the roles of multiple outcomes in the conditional independence assumption are symmetric, hence the name parallel outcomes. We show nonparametric identifiability with at least three parallel outcomes and provide parametric estimation tools under a set of linear structural equation models. Our proposal is evaluated through a set of synthetic and real data analyses.
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来源期刊
Biometrika
Biometrika 生物-生物学
CiteScore
5.50
自引率
3.70%
发文量
56
审稿时长
6-12 weeks
期刊介绍: Biometrika is primarily a journal of statistics in which emphasis is placed on papers containing original theoretical contributions of direct or potential value in applications. From time to time, papers in bordering fields are also published.
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