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. 2025 Mar;19(1):45-65.
doi: 10.1080/17437199.2024.2402809. Epub 2024 Sep 27.

How to develop causal directed acyclic graphs for observational health research: a scoping review

Louise Poppe  1 , Johan Steen  2   3   4 , Wen Wei Loh  5   6 , Geert Crombez  7 , Fien De Block  1   8 , Noortje Jacobs  1   9 , Peter W G Tennant  10   11 , Jelle Van Cauwenberg  1   12 , Annick L De Paepe  7
Affiliations

How to develop causal directed acyclic graphs for observational health research: a scoping review

Louise Poppe et al. Health Psychol Rev. 2025 Mar.

Abstract

Causal directed acyclic graphs (DAGs) serve as intuitive tools to visually represent causal relationships between variables. While they find widespread use in guiding study design, data collection and statistical analysis, their adoption remains relatively rare in the domain of psychology. In this paper we describe the relevance of DAGs for health psychology, review guidelines for developing causal DAGs, and offer recommendations for their development. A scoping review searching for papers and resources describing guidelines for DAG development was conducted. Information extracted from the eligible papers and resources (n = 11) was categorised, and results were used to formulate recommendations. Most records focused on DAG development for data analysis, with similar steps outlined. However, we found notable variations on how to implement confounding variables (i.e., sequential inclusion versus exclusion). Also, how domain knowledge should be integrated in the development process was scarcely addressed. Only one paper described how to perform a literature search for DAG development. Key recommendations for causal DAG development are provided and discussed using an illustrative example.

Keywords: Directed acyclic graph; causal diagram; causal inference; development; guidelines; recommendations.

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Conflict of interest statement

PWGT is a director of Causal Thinking Ltd, which provides causal inference research and training. PWGT may therefore benefit from any study that demonstrates the value of causal inference methods.

Figures

Figure 1.
Figure 1.
Basic definitions and graphical rules for Directed Acyclic Graphs. Notes. Measurement bias can be visualised using different structures. Interested readers are referred to Chapter 9 of Hernán and Robins (Hernán & Robins, 2020).
Figure 2.
Figure 2.
Panel A displays a feedback loop. Panel B shows the corresponding causal DAG encoding the temporal nature underlying the feedback loop. Notes. ‘k’ refers to timepoint k.
Figure 3.
Figure 3.
Completed PRISMA flow diagram.
Figure 4.
Figure 4.
DAG depicting the exposure and outcome. Notes. https://dagitty.net/mgFapKirJ The green colour of the arrow indicates this edge captures an open directed path.
Figure 5.
Figure 5.
DAG with common causes. Notes. https://dagitty.net/mUqwRMept Arrows lying on an open undirected path are indicated in red.
Figure 6.
Figure 6.
DAG including a collider. Notes. https://dagitty.net/mocqpRgwk
Figure 7.
Figure 7.
DAG representing measurement bias. Notes: https://dagitty.net/mWeagRxdW
See this image and copyright information in PMC

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