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：

---

纵向观测数据中的因果关系：一种综合建模方法。


许多心理学研究依赖于观察研究的数据，而传统上不允许进行因果解释。然而，已经开发了一系列统计学和计算科学方法来从相关数据推断因果关系。基于对因果关系的干预性和时间限制概念整合的概念和理论考虑，我们着手设计并实证测试一种新方法来识别纵向相关数据中的潜在因果因素。提出了一组有原则且有代表性的模拟和说明性应用，用于在大型队列研究中识别认知发展的早期决定因素。模拟结果说明了在观测数据中发现因果因素的潜力，但也存在局限性。在说明性应用中，确定了 5 岁儿童早期生命认知能力的可能决定因素。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，但也强调了其局限性以及潜在的误用和误解。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。