Implementation of G-Computation on a Simulated Data Set: Demonstration of a Causal Inference Technique

Jonathan M. Snowden, Division of Epidemiology, University of California, Berkeley
Sherri Rose, Division of Biostatistics, University of California, Berkeley
Kathleen M. Mortimer, Division of Epidemiology, University of California, Berkeley

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.