多效应变量的回顾性因果推理

IF 2.4 2区 数学 Q2 BIOLOGY
Biometrika Pub Date : 2023-09-14 DOI:10.1093/biomet/asad056
Wei Li, Zitong Lu, Jinzhu Jia, Min Xie, Zhi Geng
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引用次数: 0

摘要

david(2000)和Pearl &Mackenzie(2018)认为,在因果推理中,推断给定结果的原因比评估原因的影响更具挑战性。Lu等人(2023)提出了一种基于后验因果效应的方法来推断单个效应变量的原因。在许多应用中,有多个影响变量,因此可以同时使用它们来更准确地推断原因。为了从多重影响中回顾性地推断原因,我们提出了基于观察证据的多元后验效应、干预效应和直接因果效应。在无混杂和单调的假设下,我们证明了多元后验因果效应的可辨识性,并给出了它们的辨识方程。所提出的方法可以应用于各种具有多效果或结果变量的研究中的因果归因、医学诊断、责备和责任。用两个例子来说明所提出的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Retrospective causal inference with multiple effect variables
Summary As highlighted in Dawid (2000) and Pearl & Mackenzie (2018), deducing the causes of given effects is a more challenging problem than evaluating the effects of causes in causal inference. Lu et al. (2023) proposed an approach for deducing causes of a single effect variable based on posterior causal effects. In many applications, there are multiple effect variables, and thus they can be used simultaneously to more accurately deduce the causes. To retrospectively deduce causes from multiple effects, we propose multivariate posterior total, intervention and direct causal effects conditional on the observed evidence. We describe the assumptions of no-confounding and monotonicity, under which we prove identifiability of the multivariate posterior causal effects and provide their identification equations. The proposed approach can be applied for causal attributions, medical diagnosis, blame and responsibility in various studies with multiple effect or outcome variables. Two examples are used to illustrate the proposed approach.
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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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