Formalizing the role of agent-based modeling in causal inference and epidemiology.

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 multi

SUBMITTER: Marshall BD 

PROVIDER: S-EPMC4351348 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature