A Generalization of the Mechanism-based Approach for Age-Period-Cohort Models.

IF 4.7 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Epidemiology Pub Date : 2025-03-01 Epub Date: 2025-01-29 DOI:10.1097/EDE.0000000000001811
Arvid Sjölander, Erin E Gabriel
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引用次数: 0

Abstract

Age-period-cohort models have a long history in epidemiology, social science, and econometrics. An important feature of these models is that they suffer from an inherent identifiability problem, due to the deterministic linear relation between age, period, and cohort. A proposed solution to this problem is the mechanism-based approach, which uses sets of mediators to identify the causal age, period, and cohort effects. Although this approach is conceptually general, previous literature has been limited to special cases and parametric identification. We derive a general nonparametric identification result, which is valid under explicit assumptions about the underlying data-generating mechanism and the set of mediators used for identification. We show how this identification result lends itself naturally to parametric estimation of the causal age, period, and cohort effects similar to the parametric G-formula estimation in causal inference.

年龄-时期-队列模型在流行病学、社会科学和计量经济学中有着悠久的历史。这些模型的一个重要特征是,由于年龄、时期和队列之间的确定性线性关系,它们存在固有的可识别性问题。针对这个问题提出的一种解决方案是基于机制的方法,它使用一组中介来确定年龄、时期和队列效应的因果关系。虽然这种方法在概念上是通用的,但以前的文献仅限于特殊情况和参数识别。我们导出了一个一般的非参数识别结果,该结果在关于底层数据生成机制和用于识别的中介集的明确假设下是有效的。我们展示了这种识别结果如何自然地适用于因果年龄、时期和队列效应的参数估计,类似于因果推理中的参数g公式估计。
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来源期刊
Epidemiology
Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
6.70
自引率
3.70%
发文量
177
审稿时长
6-12 weeks
期刊介绍: Epidemiology publishes original research from all fields of epidemiology. The journal also welcomes review articles and meta-analyses, novel hypotheses, descriptions and applications of new methods, and discussions of research theory or public health policy. We give special consideration to papers from developing countries.
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