Constructing Causal Life-Course Models: Comparative Study of Data-Driven and Theory-Driven Approaches.
Constructing Causal Life-Course Models: Comparative Study of Data-Driven and Theory-Driven Approaches.
复制标题
构建因果生命历程模型：数据驱动和理论驱动方法的比较研究。
DOI：
10.1093/aje/kwad144
复制
发表时间：
2023
影响因子：
5
通讯作者：
Osler,Merete
中科院分区：
文献类型：
--
作者：
Petersen,AnneHelby;Ekstrøm,ClausThorn;Spirtes,Peter;Osler,Merete
生命过程流行病学依赖于指定复杂的（因果）模型，描述变量如何随着时间的推移相互作用。传统上，这种模型是通过研读现有的理论和以前的研究来构建的。通过比较数据驱动和理论驱动的模型，我们研究了数据驱动的因果发现算法是否可以在这个过程中提供帮助。我们专注于丹麦男性队列的纵向数据集（Metropolit研究，1953-2017）。理论驱动模型由2名学科领域专家构建。数据驱动的模型是通过使用时间的彼得-克拉克（TPC）算法。TPC算法利用嵌入在生命过程数据中的时间信息。我们发现，数据驱动的模型恢复了一些，但不是全部，因果关系包括在理论驱动的专家模型。数据驱动的方法特别擅长识别专家高度信任的直接因果关系。此外，在事后评估中，我们发现数据驱动模型提出的但理论驱动模型中未包含的大多数直接因果关系都是合理的。因此，数据驱动模型可能会提出新的或被专家忽视的其他有意义的因果假设。总之，数据驱动的方法可以帮助在生命过程流行病学因果模型的构建，并结合数据驱动和理论驱动的方法可以导致更强大的模型。
Life-course epidemiology relies on specifying complex (causal) models that describe how variables interplay over time. Traditionally, such models have been constructed by perusing existing theory and previous studies. By comparing data-driven and theory-driven models, we investigated whether data-driven causal discovery algorithms can help in this process. We focused on a longitudinal data set on a cohort of Danish men (the Metropolit Study, 1953–2017). The theory-driven models were constructed by 2 subject-field experts. The data-driven models were constructed by use of the temporal Peter-Clark (TPC) algorithm. The TPC algorithm utilizes the temporal information embedded in life-course data. We found that the data-driven models recovered some, but not all, causal relationships included in the theory-driven expert models. The data-driven method was especially good at identifying direct causal relationships that the experts had high confidence in. Moreover, in a post hoc assessment, we found that most of the direct causal relationships proposed by the data-driven model but not included in the theory-driven model were plausible. Thus, the data-driven model may propose additional meaningful causal hypotheses that are new or have been overlooked by the experts. In conclusion, data-driven methods can aid causal model construction in life-course epidemiology, and combining both data-driven and theory-driven methods can lead to even stronger models.