Arnold, KF orcid.org/0000-0002-0911-5029, Harrison, WJ orcid.org/0000-0003-1180-8112, Heppenstall, AJ orcid.org/0000-0002-0663-3437 et al. (1 more author) (2019) DAG-informed regression modelling, agent-based modelling, and microsimulation modelling: A critical comparison of methods for causal inference. International Journal of Epidemiology, 48 (1). pp. 243-253. ISSN 0300-5771
Abstract
The current paradigm for causal inference in epidemiology relies primarily on the evaluation of counterfactual contrasts via statistical regression models informed by graphical causal models (often in the form of directed acyclic graphs, or DAGs) and their underlying mathematical theory. However, there have been growing calls for supplementary methods, and one such method that has been proposed is agent-based modelling due to its potential for simulating counterfactuals. However, within the epidemiological literature there currently exists a general lack of clarity regarding what exactly agent-based modelling is (and is not) and, importantly, how it differs from microsimulation modelling – perhaps its closest methodological comparator. We clarify this distinction by briefly reviewing the history of each method, which provides context for their similarities and differences, and casts light on the types of research questions that they have evolved (and thus are well-suited) to answering; we do the same for DAG-informed regression methods. The distinct historical evolutions of DAG-informed regression modelling, microsimulation modelling, and agent-based modelling have given rise to distinct features of the methods themselves, and provide a foundation for critical comparison. Not only are the three methods well-suited to addressing different types of causal questions, but in doing so they place differing levels of emphasis on fixed and random effects, and also tend to operate on different timescales and in different timeframes.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2018. Published by Oxford University Press on behalf of the International Epidemiological Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | causal inference; counterfactuals; directed acyclic graphs; agent-based modelling; microsimulation modelling |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Clinical & Population Science Dept (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Oct 2018 12:07 |
Last Modified: | 25 Jun 2023 21:33 |
Status: | Published |
Publisher: | Oxford University Press |
Identification Number: | 10.1093/ije/dyy260 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:137566 |