Formalizing the role of agent-based modeling in causal inference and epidemiology
Open accessAbstract
Calls for the adoption of complex systems approaches, including agent-based modeling, in the field of epidemiology have largely centered on the potential for such methods to examine complex disease etiologies, which are characterized by feedback behavior, interference, threshold dynamics, and multiple interacting causal effects. However, considerable theoretical and practical issues impede the capacity of agent-based methods to examine and evaluate causal effects and thus illuminate new areas for intervention. We build on this work by describing how agent-based models can be used to simulate counterfactual outcomes in the presence of complexity. We show that these models are of particular utility when the hypothesized causal mechanisms exhibit a high degree of interdependence between multiple causal effects and when interference (i.e., one person's exposure affects the outcome of others) is present and of intrinsic scientific interest. Although not without challenges, agent-based modeling (and complex systems methods broadly) represent a promising novel approach to identify and evaluate complex causal effects, and they are thus well suited to complement other modern epidemiologic methods of etiologic inquiry.
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Treatment effect estimates from agent-based models are causal because counterfactual scenarios can be generated without limit.
“The treatment effect estimates so obtained are causal, in the sense that counterfactual scenarios can be created ad infinitum (Marshall & Galea, 2015)”
From Syndemics: a theory in search of data or data in search of a theory? (2018) · cites this paper as Marshall & Galea, 2015
The “Simulating Counterfactuals” section says ABMs can compare any number of hypothetical scenarios and repeatedly generate outputs with a counterfactual interpretation. But the Conclusions figure and “Defining Assumptions to Estimate Agent-Based Causal Effects” state that estimating causal effects also requires ergodicity and correct model specification. The claim overstates the source by making the ability to generate scenarios alone sufficient. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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- Authors
- Marshall, B. D. L., & Galea, S.
- Affiliations
- Brown University (US)
- Corresponding author
- Marshall, Brandon D. L.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-12-05
- Volume
- 181
- Issue
- 2
- Pages
- 92-99
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- PMC
- PMC4351348
- OpenAlex
- W2170588867
- Cited by
- 192
- References
- 48
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 11 · 2023: 20 · 2022: 13 · 2021: 22 · 2020: 14 · 2019: 15 · 2018: 26 · 2017: 22 · 2016: 18 · 2015: 9 · 2014: 3
- FWCI
- 18.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- American Journal of Epidemiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Brown University
- Center for AIDS Research, University of Washington
- National Institute of Allergy and Infectious Diseases (P30AI042853)
- Mailman School of Public Health, Columbia University
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mathematical and Theoretical Epidemiology and Ecology Models
- Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
- Keywords
- agent-based modeling, causal inference, complex systems, potential outcomes, interference, causal mechanisms, intervention evaluation
- MeSH terms
- Environment, Epidemiologic Methods, Humans, Models, Theoretical, Causality, Confounding Factors, Epidemiologic, Social Determinants of Health
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