Compara mètodes

	Model Estructural Marginal per a l'Avaluació de Polítiques	Estimació Doblement Robusta (AIPW)
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2000	2005
Autor original	James M. Robins, Miguel A. Hernan, Babette Brumback	Robins & Rotnitzky; Bang & Robins
Tipus	Causal inference / weighted regression	Semiparametric causal estimator
Font seminal	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550–560.	Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129.
Àlies	MSM for policy evaluation, policy MSM, causal MSM, structural policy weighting model	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Relacionats	6	5
Resum	A Policy Evaluation Marginal Structural Model (MSM) is a causal inference framework that estimates the population-average effect of a policy by using inverse probability weighting to create a pseudo-population in which treatment assignment is independent of measured confounders, enabling unbiased comparison of potential outcomes under different policy scenarios from observational data.	Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified.