Using Causal Loop Diagrams (CLDs) to inform the development of Directed Acyclic Graphs (DAGs)

I Avila-Palencia

doi:10.1093/eurpub/ckad160.451

Abstract

Complex systems-based approaches, like causal loop diagrams (CLDs), are increasingly being used in population health studies. Traditionally, directed acyclic graphs (DAGs) have been frequently used in causal inference methods in population health studies to define analysis plans and identify potential biases. The use of those two methodologies has been suggested to be incompatible due to DAGs being apparently unsuitable for modelling systems containing feedback loops, a common feature of complex systems. In this presentation we will detail the steps and decisions that a research team could follow to translate a CLD into a series of DAGs.

Original language	English
Number of pages	1
Journal	European Journal of Public Health
Volume	33
Issue number	Supplement_2
DOIs	https://doi.org/10.1093/eurpub/ckad160.451
Publication status	Published - 24 Oct 2023

Keywords

Public Health, Environmental and Occupational Health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1093/eurpub/ckad160.451Licence: CC BY-NC

Using Causal Loop Diagrams (CLDs) to inform the development of Directed Acyclic Graphs (DAGs)
Copyright 2023 the authors. This is an open access Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits use, distribution and reproduction for non-commercial purposes, provided the author and source are cited.
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Cite this

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