G-estimation is a flexible, semiparametric approach for estimating exposure effects in epidemiologic studies. It has several underappreciated advantages over other propensity score-based methods popular in epidemiology, which we review in this article. However, it is rarely used in practice, due to a lack of off-the-shelf software. To rectify this, we show a simple trick for obtaining G-estimators of causal risk ratios using existing generalized estimating equations software. We extend the procedure to more complex settings with time-varying confounders.
