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- Title
Formalizing the Role of Agent-Based Modeling in Causal Inference and Epidemiology.
- Authors
Marshall, Brandon D. L.; Galea, Sandro
- Abstract
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.
- Subjects
EPIDEMIOLOGY research methodology; HYPOTHESIS; ATTRIBUTION (Social psychology); COMPUTER simulation; DISEASES; RESEARCH; SYSTEMS theory; MATHEMATICAL variables
- Publication
American Journal of Epidemiology, 2015, Vol 181, Issue 2, p92
- ISSN
0002-9262
- Publication type
Article
- DOI
10.1093/aje/kwu274