Much research in psychology relies on data from observational studies that traditionally do not allow for causal interpretation. However, a range of approaches in statistics and computational sciences have been developed to infer causality from correlational data. Based on conceptual and theoretical considerations on the integration of interventional and time-restrainment notions of causality, we set out to design and empirically test a new approach to identify potential causal factors in longitudinal correlational data. A principled and representative set of simulations and an illustrative application to identify early-life determinants of cognitive development in a large cohort study are presented. The simulation results illustrate the potential but also the limitations for discovering causal factors in observational data. In the illustrative application, plausible candidates for early-life determinants of cognitive abilities in 5-year-old children were identified. Based on these results, we discuss the possibilities of using exploratory causal discovery in psychological research but also highlight its limits and potential misuses and misinterpretations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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纵向观测数据中的因果关系：一种综合建模方法。


心理学的许多研究都依赖于传统上不允许因果解释的观察性研究数据。然而，统计学和计算科学中的一系列方法已经被开发出来，可以从相关数据中推断出因果关系。基于对因果关系的干预和时间限制概念整合的概念和理论考虑，我们着手设计和实证测试一种新方法，以识别纵向相关数据中的潜在因果因素。在一项大型队列研究中，提出了一组有原则且具有代表性的模拟和一个说明性应用程序，以确定认知发展的早期生活决定因素。模拟结果说明了在观测数据中发现致病因素的潜力，但也说明了局限性。在说明性应用中，确定了 5 岁儿童早期认知能力决定因素的合理候选者。基于这些结果，我们讨论了在心理学研究中使用探索性因果发现的可能性，但也强调了它的局限性和潜在的误用和误解。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。