Implementation of G-Computation on a Simulated Data Set: Demonstration of a Causal Inference Technique
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.
Jonathan M. Snowden, Sherri Rose, and Kathleen M. Mortimer. "Implementation of G-Computation on a Simulated Data Set: Demonstration of a Causal Inference Technique" American Journal of Epidemiology 173 (2011).