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 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.

Keywords: agent-based models; causal inference; complex systems; complexity; population health; public health.

Figure 1.

Epidemiologic methods to estimate average causal effects. The black and white dashed areas indicate the subgroup of the population with disease; the dark gray areas indicate exposed/treated subjects; and the dotted gray area indicates that the exposure is present in the population before manipulation. In an observational study, average causal effects are estimable under the assumptions of exchangeability, consistency, positivity, and correct model specification. In a simulation study, causal effects are estimable under the assumptions of ergodicity and correct model specification.