Comparer des méthodes

	Pondération Bayésienne par l'Inverse de la Probabilité	Modèle structurel marginal (MSM)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2015	2000
Auteur d'origine	Saarela, Stephens, Moodie & Klein (2015); Liao & Zigler (2020)	James M. Robins, Miguel A. Hernan, Babette Brumback
Type	Bayesian causal weighting estimator	Causal model / semiparametric weighting
Source fondatrice	Saarela, O., Stephens, D. A., Moodie, E. E. M., & Klein, M. B. (2015). On risk prediction and characterisation of treatment effects in a Bayesian framework using the propensity score. Statistics in Medicine, 34(14), 2170-2185.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Alias	Bayesian IPW, BIPW, Bayesian propensity-weighted estimation, Bayesian marginal structural weighting	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Apparentées	6	5
Résumé	Bayesian Inverse Probability Weighting (Bayesian IPW) extends the classical IPW estimator by placing prior distributions over the propensity-score model parameters and propagating that uncertainty into the causal-effect estimate. The result is a posterior distribution for the average treatment effect that fully accounts for both propensity-score estimation uncertainty and outcome-model uncertainty, enabling credible-interval inference rather than relying on asymptotic approximations.	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.