An introduction to directed acyclic graphs in trauma research.

Abstract

Directed acyclic graphs (DAGs) are visual representations of the presumed causal structure of an empirical research data set. They are important tools for researchers but have been used rarely in the psychological trauma literature. The purpose of this article is to explain what DAGs are and why (and how) they are useful for trauma researchers. We first describe the utility of DAGs for making causal assumptions explicit, identifying causal effects, and preventing bias. Basic definitions and rules governing the use of DAGs are presented using a hypothetical DAG. We explain why conditioning on a variable, for example, by controlling for it in a multivariable model, can in some circumstances actually introduce bias and not prevent it. We also provide references for topics related to DAGs that are beyond the scope of this introductory article. DAGs are illustrated using the example of the effect of posttraumatic stress disorder (PTSD) on Parkinson's disease. We demonstrate that a multivariable model controlling for all covariates that are being considered introduces bias and would make it impossible to identify the causal effect of PTSD on Parkinson's disease. DAGs can help trauma researchers to understand when they can and when they cannot draw causal conclusions based on research data. This introduction to DAGs should help readers understand their use in the articles on marginal structural models, causal mediation analysis, and instrumental variable methods in this special section, Causal inference and agent-based modeling in trauma research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).