Parametric g-Formula
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.
Source record
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- 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. · DOI 10.1016/0270-0255(86)90088-6
- Keil, A. P., Edwards, J. K., Richardson, D. B., Naimi, A. I., & Cole, S. R. (2014). The parametric g-formula for time-to-event data: intuition and a worked example. Epidemiology, 25(6), 889-897. · DOI 10.1097/EDE.0000000000000160
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