Bridging System Dynamics and Causal Epidemiology: An Opportunity for Both Fields

ABSTRACT This paper examines the potential benefits of a deeper integration between system dynamics and causal inference as applied in epidemiology. We offer four suggestions for bridging these fields: two for what system dynamics can offer and two for what system dynamics stands to gain. First, we discuss the use of system dynamics to develop simulation models that emphasize feedback, (unobserved) dynamics, and multiscale interactions. Second, we note that the formalized participatory methods from system dynamics could help strengthen causal models in epidemiology. Third, we advocate for outlining and explicitly stating causal assumptions relevant to system dynamics research. Lastly, we suggest enhancing the causal structure of system dynamics models by triangulating participatory methods and literature review with data‐driven causal discovery. Through these suggestions, we envision the development of more credible and transparent causal models of complex health problems.