Implementation of G-computation on a simulated data set: Demonstration of a causal inference technique

Jonathan M. Snowden, Sherri Rose, Kathleen M. Mortimer

Research output: Contribution to journalArticle

117 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)731-738
Number of pages8
JournalAmerican journal of epidemiology
Volume173
Issue number7
DOIs
StatePublished - Apr 1 2011

Keywords

  • air pollution
  • asthma
  • causality
  • methods
  • regression analysis

ASJC Scopus subject areas

  • Epidemiology

Fingerprint Dive into the research topics of 'Implementation of G-computation on a simulated data set: Demonstration of a causal inference technique'. Together they form a unique fingerprint.

  • Cite this