Causal Knowledge as a Prerequisite for Interrogating Bias: Reflections on Hernan et al. 20 Years Later

In their seminal 2002 paper, Causal Knowledge as a Prerequisite for Confounding Evaluation: An Application to Birth Defects Epidemiology, Hernan et al. (Am J Epidemiol. 2002;155(2):176-184) emphasized the importance of using theory rather than data to guide confounding control, focusing on colliders as variables that share characteristics with confounders but whose control may actually introduce bias into analyses. In this commentary, we propose that the importance of this paper stems from the connection the authors made between nonexchangeability as the ultimate source of bias and structural representations of bias using directed acyclic graphs. This provided both a unified approach to conceptualizing bias and a means of distinguishing between different sources of bias, particularly confounding and selection bias. Drawing on examples from the paper, we also highlight unresolved questions about the relationship between collider bias, selection bias, and generalizability and argue that causal knowledge is a prerequisite not only for identifying confounders but also for developing any hypothesis about potential sources of bias.

Automated summary

The paper emphasizes the importance of theory in confounding control, specifically focusing on colliders and bias control. By utilizing directed acyclic graphs, the authors provide a unified conceptualization of bias and differentiate between different sources of bias.