Causal inference algorithms can be useful in life course epidemiology

Objectives. Life course epidemiology attempts to unravel causal relationships between variables observed over time. Causal relationships can be represented as directed acyclic graphs. This article explains the theoretical concepts of the search algorithms used for finding such representations, discusses various types of such algorithms, and exemplifies their use in the context of obesity and insulin resistance. Study Design and Setting. We investigated possible causal relations between gender, birth weight, waist circumference, and blood glucose level of 4,081 adult participants of the Prevention of REnal and Vascular ENd-stage Disease study. The latter two variables were measured at three time points at intervals of about 3 years. Results. We present the resulting causal graphs, estimate parameters of the corresponding structural equation models, and discuss usefulness and limitations of this methodology. Conclusion. As an exploratory method, causal graphs and the associated theory can help construct possible causal models underlying observational data. In this way, the causal search algorithms provide a valuable statistical tool for life course epidemiological research. Keywords: Causality; Causal graphs; Search algorithms; Life course epidemiology; Metabolic syndrome; Cohort studie