방법 비교

	Marginal Structural Model (IPTW)	Targeted Maximum Likelihood Estimation (Epidemiology)
분야	Social Epidemiology	Social Epidemiology
계열	Process / pipeline	Machine learning
기원 연도	2000	2006
창시자	James M. Robins, Miguel A. Hernán & Babette Brumback	Mark J. van der Laan & Daniel Rubin; Megan Schuler & Sherri Rose (epidemiology tutorial)
유형	Reweighting pipeline for time-varying confounding affected by prior treatment	Doubly-robust substitution estimator with a targeting update and machine-learning nuisance models
원전	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	van der Laan, M. J., & Rubin, D. (2006). Targeted maximum likelihood learning. The International Journal of Biostatistics, 2(1), Article 11.
별칭	MSM with IPTW, Inverse-Probability-of-Treatment-Weighted Marginal Structural Model, IPTW Marginal Structural Model, Robins Marginal Structural Model	TMLE, Targeted Minimum Loss-Based Estimation, Doubly-Robust TMLE, Targeted Learning
관련	3	3
요약	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.	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.