Linganisha mbinu

	Marginal Structural Model (IPTW)	Parametric g-Formula
Nyanja	Social Epidemiology	Social Epidemiology
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2000	1986
Mwanzilishi	James M. Robins, Miguel A. Hernán & Babette Brumback	James M. Robins; Ashley I. Naimi, Alexander P. Keil et al. (applied tutorial)
Aina	Reweighting pipeline for time-varying confounding affected by prior treatment	Counterfactual simulation pipeline for time-varying treatment regimes
Chanzo asilia	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Robins, J. M. (1986). A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512.
Majina mbadala	MSM with IPTW, Inverse-Probability-of-Treatment-Weighted Marginal Structural Model, IPTW Marginal Structural Model, Robins Marginal Structural Model	g-Computation Formula, Robins' g-Formula, Parametric g-Computation, Generalized Computation Algorithm Formula
Zinazohusiana	3	3
Muhtasari	Marginal structural models, introduced by Robins, Hernán, and Brumback in 2000, are causal models for the mean of a counterfactual outcome under a treatment regime, estimated by inverse-probability-of-treatment weighting. They solve the same problem as the g-formula — estimating the effect of a time-varying exposure when time-varying confounders are themselves affected by prior treatment — but through a different device: instead of modeling the outcome and confounder processes, they reweight each person by the inverse of their probability of receiving the treatment history they actually received. This creates a pseudo-population in which treatment is, by construction, unconfounded by the measured covariates, so a simple weighted regression recovers the causal effect. The companion 2000 paper applying the method to zidovudine and HIV survival showed its practical payoff. In social epidemiology, MSMs with IPTW are standard for the cumulative effects of time-varying social exposures.	The parametric g-formula is the estimator James Robins introduced in 1986 to recover the causal effect of a time-varying exposure when time-varying confounders are themselves affected by past exposure — a setting where standard regression adjustment is guaranteed to give the wrong answer. Rather than conditioning on the troublesome confounders directly, the g-formula reconstructs the entire counterfactual world: it parametrically estimates how confounders and the outcome evolve over time, then Monte-Carlo simulates what would have happened to the population under a hypothetical exposure regime such as 'always exposed' versus 'never exposed.' Keil and colleagues' 2014 worked tutorial for time-to-event data made the algorithm concrete for epidemiologists. In social epidemiology it is the workhorse for questions like the cumulative effect of sustained neighborhood deprivation, employment, or income trajectories on health, where mediators and confounders are tangled across time.