Using Causal Loop Diagrams (CLDs) to inform the development of Directed Acyclic Graphs (DAGs)

Abstract

Abstract Complex systems-based approaches, like causal loop diagrams (CLDs), are increasingly being used in population health studies. Traditionally, directed acyclic graphs (DAGs) have been frequently used in causal inference methods in population health studies to define analysis plans and identify potential biases. The use of those two methodologies has been suggested to be incompatible due to DAGs being apparently unsuitable for modelling systems containing feedback loops, a common feature of complex systems. In this presentation we will detail the steps and decisions that a research team could follow to translate a CLD into a series of DAGs.