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Comment
. 2015 Jan 15;181(2):103-5.
doi: 10.1093/aje/kwu272. Epub 2014 Dec 5.

Invited commentary: Agent-based models for causal inference—reweighting data and theory in epidemiology

Miguel A Hernán
Comment

Invited commentary: Agent-based models for causal inference—reweighting data and theory in epidemiology

Miguel A Hernán. Am J Epidemiol. .

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.

Keywords: agent-based models; causal inference; parametric g-formula.

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Figures

Figure 1.
Figure 1.
The relative position of several scientific disciplines along the causal inference spectrum according to the relative weights of data and theory.
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References

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