Designing Causal Diagrams for Theoretical Reasoning and Measurement. Visualisations from Life-Course Research

Abstract Directed acyclic graphs (DAGs) have become popular as graphical representations of causal relationships. In practice, DAGs have proven to be particularly helpful for selecting appropriate control variables in causally oriented analytical models. While confirming such usefulness, this paper also aims to highlight another, often neglected, aspect: the potential for causal diagrams to support the formulation of theories and corresponding hypotheses. This is particularly the case when diagrams have certain graphical properties, and suggestions are offered regarding how this can be achieved. Examples are drawn from the field of life-course research, with the intention of better integrating the visual techniques prevalent in life-course research with DAG-style causal diagrams. While standard causal diagrams may not pay sufficient attention to certain relevant aspects, graphically enhanced causal diagrams can be quite productive for theory development and the analysis of existing life-course data. They are also useful for conceptualising new causally oriented studies. This paper illustrates suitable approaches with original and adapted visualisations.