title: Implementation of G-computation on a simulated data set: demonstration of a causal inference technique.
abstract: The growing body of work in the epidemiology literature focused on G-computation includes theoretical explanations of the method but very few simulations or examples of application. The small number of G-computation analyses in the epidemiology literature relative to other causal inference approaches may be partially due to a lack of didactic explanations of the method targeted toward an epidemiology audience. The authors provide a step-by-step demonstration of G-computation that is intended to familiarize the reader with this procedure. The authors simulate a data set and then demonstrate both G-computation and traditional regression to draw connections and illustrate contrasts between their implementation and interpretation relative to the truth of the simulation protocol. A marginal structural model is used for effect estimation in the G-computation example. The authors conclude by answering a series of questions to emphasize the key characteristics of causal inference techniques and the G-computation procedure in particular.
authors.affiliation: Division of Epidemiology, School of Public Health, University of California-Berkeley, 1918 University Avenue, Berkeley, CA 94704, USA. jsnowden@berkeley.edu
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Snowden
authors.forename: Sherri
authors.initials: S
authors.lastname: Rose
authors.forename: Kathleen M
authors.initials: KM
authors.lastname: Mortimer
mesh_terms: D015984:Causality; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D015340:Epidemiologic Research Design; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis