A Graphical Catalog of Threats to Validity

Directed acyclic graphs (DAGs), a prominent tool for expressing assumptions in epidemiologic research, are most useful when the hypothetical data generating structure is correctly encoded. Understanding a study's data generating structure and translating that data structure into a DAG can be challenging, but these skills are often glossed over in training. Campbell and Stanley's framework for causal inference has been extraordinarily influential in social science training programs but has received less attention in epidemiology. Their work, along with subsequent revisions and enhancements based on practical experience conducting empirical studies, presents a catalog of 37 threats to validity describing reasons empirical studies may fail to deliver causal effects. We interpret most of these threats to study validity as suggestions for common causal structures. Threats are organized into issues of statistical conclusion validity, internal validity, construct validity, or external validity. To assist epidemiologists in drawing the correct DAG for their application, we map the correspondence between threats to validity and epidemiologic concepts that can be represented with DAGs. Representing these threats as DAGs makes them amenable to formal analysis with d-separation rules and breaks down cross-disciplinary language barriers in communicating methodologic issues.