title: Invited commentary: G-computation--lost in translation?
abstract: In this issue of the Journal, Snowden et al. (Am J Epidemiol. 2011;173(7):731-738) give a didactic explanation of G-computation as an approach for estimating the causal effect of a point exposure. The authors of the present commentary reinforce the idea that their use of G-computation is equivalent to a particular form of model-based standardization, whereby reference is made to the observed study population, a technique that epidemiologists have been applying for several decades. They comment on the use of standardized versus conditional effect measures and on the relative predominance of the inverse probability-of-treatment weighting approach as opposed to G-computation. They further propose a compromise approach, doubly robust standardization, that combines the benefits of both of these causal inference techniques and is not more difficult to implement.
authors.affiliation: Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281, Ghent, Belgium. stijn.vansteelandt@ugent.be
authors.forename: Stijn
authors.initials: S
authors.lastname: Vansteelandt
authors.forename: Niels
authors.initials: N
authors.lastname: Keiding
mesh_terms: D015984:Causality; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D015340:Epidemiologic Research Design; D006801:Humans; D015233:Models, Statistical; D011336:Probability; D012044:Regression Analysis