Compara mètodes

	Four-Way Decomposition	Parametric g-Formula
Camp	Social Epidemiology	Social Epidemiology
Família	Process / pipeline	Process / pipeline
Any d'origen	2014	1986
Autor original	Tyler J. VanderWeele	James M. Robins; Ashley I. Naimi, Alexander P. Keil et al. (applied tutorial)
Tipus	Counterfactual decomposition pipeline for total effects	Counterfactual simulation pipeline for time-varying treatment regimes
Font seminal	VanderWeele, T. J. (2014). A unification of mediation and interaction: a four-way decomposition. Epidemiology, 25(5), 749-761.	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.
Àlies	4-Way Decomposition, VanderWeele Four-Way Decomposition, Mediation-Interaction Decomposition, Unification of Mediation and Interaction	g-Computation Formula, Robins' g-Formula, Parametric g-Computation, Generalized Computation Algorithm Formula
Relacionats	3	3
Resum	The four-way decomposition, introduced by Tyler VanderWeele in 2014, unifies the two great themes of effect analysis — mediation and interaction — into a single, exhaustive partition of a total causal effect. Any total effect of an exposure on an outcome can be split into exactly four pieces: a controlled direct effect (neither mediation nor interaction), a reference interaction (interaction but no mediation), a mediated interaction (both mediation and interaction at once), and a pure indirect effect (mediation but no interaction). These four components are mutually exclusive and add up to the total effect, and they nest the familiar two-way and three-way decompositions as special cases. Formalized in counterfactual notation and developed at book length in VanderWeele's 2015 Explanation in Causal Inference, the method gives social epidemiologists a precise vocabulary for asking how much of an exposure's effect runs through a mediator, how much depends on the exposure and mediator acting together, and how much is direct.	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.