Comparer des méthodes

	Évaluation de politiques par pondération par l'inverse de la probabilité	Modèle structurel marginal (MSM)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1952 (IPW origin); 2000s (policy evaluation application)	2000
Auteur d'origine	Horvitz & Thompson (1952); extended to causal policy settings by Robins, Hernan & Brumback (2000) and Imbens & Wooldridge (2009)	James M. Robins, Miguel A. Hernan, Babette Brumback
Type	Reweighting estimator for causal policy analysis	Causal model / semiparametric weighting
Source fondatrice	Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Alias	IPW policy evaluation, propensity-weighted policy analysis, inverse probability of treatment weighting	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Apparentées	6	5
Résumé	Policy evaluation inverse probability weighting (IPW) uses estimated propensity scores to reweight observed units so that the weighted sample mimics a randomised experiment. Each unit is weighted by the inverse of its probability of receiving the policy, creating a pseudo-population in which treatment assignment is independent of observed covariates and the average treatment effect (ATE) can be read off directly.	A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail.