方法对比

	Targeted Maximum Likelihood Estimation (Epidemiology)	Parametric g-Formula
领域	Social Epidemiology	Social Epidemiology
方法族	Machine learning	Process / pipeline
起源年份	2006	1986
提出者	Mark J. van der Laan & Daniel Rubin; Megan Schuler & Sherri Rose (epidemiology tutorial)	James M. Robins; Ashley I. Naimi, Alexander P. Keil et al. (applied tutorial)
类型	Doubly-robust substitution estimator with a targeting update and machine-learning nuisance models	Counterfactual simulation pipeline for time-varying treatment regimes
开创性文献	van der Laan, M. J., & Rubin, D. (2006). Targeted maximum likelihood learning. The International Journal of Biostatistics, 2(1), Article 11.	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.
别名	TMLE, Targeted Minimum Loss-Based Estimation, Doubly-Robust TMLE, Targeted Learning	g-Computation Formula, Robins' g-Formula, Parametric g-Computation, Generalized Computation Algorithm Formula
相关	3	3
摘要	Targeted maximum likelihood estimation (TMLE), introduced by Mark van der Laan and Daniel Rubin in 2006, is a doubly-robust, semiparametric framework for estimating causal effects that marries machine learning with the theory of efficient influence functions. It begins by flexibly estimating two nuisance quantities — the outcome regression and the propensity score — typically with an ensemble 'super learner,' and then performs a clever targeting step that nudges the outcome model in exactly the direction needed to remove plug-in bias for the causal parameter of interest. The result is a substitution estimator that is consistent if either the outcome model or the propensity model is correct (double robustness) and asymptotically efficient if both are, all while permitting aggressive data-adaptive estimation. Schuler and Rose's 2017 American Journal of Epidemiology tutorial brought TMLE to a broad epidemiologic audience, including social-epidemiologic applications where confounding structures are complex and functional forms unknown.	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.