Drawing credible directed acyclic graphs for causal inference.

What the paper says

Causal directed acyclic graphs (DAGs) are intelligible representations of real-world data-generating processes that facilitate causal inference by providing (automatized) guidance for assessing whether a causal effect is identified with the observed data and for selecting covariates that remove most, if not all confounding bias. However, less attention has been paid to the process of <i>constructing</i> causal DAGs. Methodological work often relies on toy examples that have limited practical utility for applied researchers working in complex contexts. This article introduces and demonstrates a stepwise, iterative procedure for drawing credible causal DAGs, which is designed to guide researchers in identifying important sources of confounding while also incorporating research design features of quasi-experiments or randomized experiments, as well as threats to validity (e.g., measurement error, treatment noncompliance). Although constructing a complete DAG that fully captures the data-generating process is difficult and rarely achievable in practice, we argue that developing a <i>credible</i> DAG-one that includes all plausible sources of confounding-is adequate for applied research. The proposed iterative drawing procedure is directly aligned with the goal of constructing credible causal DAGs. (PsycInfo Database Record (c) 2026 APA, all rights reserved).