The g formula is a cornerstone method for estimating causal effects of time-varying treatments using longitudinal observational data in the presence of time-varying confounders that are affected by prior treatment. Standard regression techniques often fail in this setting because adjustment for such covariates can distort the very effects under study. The g-formula addresses this problem by expressing the mean potential outcome under specified static or dynamic treatment regimes as a function of the joint distribution of covariates, treatments and outcomes, which can be approximated via parametric or semi-parametric models and simulation. This review presents the g-formula, emphasizing intuitive explanations. After outlining the causal framework and the core identification assumptions-consistency, sequential exchangeability and positivity-the article describes practical parametric g-formula: model specification for covariate and outcome processes, implementation via forward simulation, and the interpretation of marginal causal contrasts between clinically relevant regimes. The g-formula is then situated within the family of g-methods alongside inverse probability weighting and targeted maximum likelihood estimation, highlighting complementary strengths and limitations. A dedicated section discusses concrete applications, including analyses of highly active antiretroviral therapy and AIDS or death, dynamic 'when to start' antiretroviral strategies in HIV and electronic health record-based evaluations of blood pressure treatment targets. Practical guidance on modelling choices, diagnostics and transparent reporting is provided to support applied researchers considering g-formula in clinical and epidemiological investigations.
Zoccali et al. (Tue,) studied this question.