title: Invited commentary: Agent-based models for causal inference—reweighting data and theory in epidemiology.
abstract: The relative weights of empirical facts (data) and assumptions (theory) in causal inference vary across disciplines. Typically, disciplines that ask more complex questions tend to better tolerate a greater role of theory and modeling in causal inference. As epidemiologists move toward increasingly complex questions, Marshall and Galea (Am J Epidemiol. 2015;181(2):92-99) support a reweighting of data and theory in epidemiologic research via the use of agent-based modeling. The parametric g-formula can be viewed as an intermediate step between traditional epidemiologic methods and agent-based modeling and therefore is a method that can ease the transition toward epidemiologic methods that rely heavily on modeling.
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Hernán
mesh_terms: D015984:Causality; D004812:Epidemiologic Methods; D006801:Humans; D008962:Models, Theoretical
keywords: agent-based models; causal inference; parametric g-formula