Journal of clinical epidemiology
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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. ⋯ 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.