The g-formula and agent-based models (ABMs) are 2 approaches used to estimate causal effects. In the current issue of the Journal, Murray et al. (Am J Epidemiol. 2017;186(2):131-142) compare the performance of the g-formula and ABMs to estimate causal effects in 3 target populations. In their thoughtful paper, the authors outline several reasons that a causal effect estimated using an ABM may be biased when parameterized from at least 1 source external to the target population. The authors have addressed an important issue in epidemiology: Often causal effect estimates are needed to inform public health decisions in settings without complete data. Because public health decisions are urgent, epidemiologists are frequently called upon to estimate a causal effect from existing data in a separate population rather than perform new data collection activities. The assumptions needed to transport causal effects to a specific target population must be carefully stated and assessed, just as one would explicitly state and analyze the assumptions required to draw internally valid causal inference in a specific study sample. Considering external validity in important target populations increases the impact of epidemiologic studies.
Keywords: Monte Carlo methods; agent-based models; causal inference; decision analysis; individual-level models; mathematical models; medical decision making; parametric g-formula.
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