Comparer des méthodes

	Modèle Structurel Marginal Bayésien	Variables Instrumentales Bayésiennes (IV Bayésienne)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2015 (Bayesian extension); 2000 (MSM foundation)	2003
Auteur d'origine	Saarela, Stephens, Moodie & Klein (Bayesian extension); Robins, Hernan & Brumback (original MSM)	Kleibergen & Zivot (2003); Lancaster (2004)
Type	Causal inference / Bayesian weighted regression	Causal inference / Bayesian estimation
Source fondatrice	Saarela, O., Stephens, D. A., Moodie, E. E. M., & Klein, M. B. (2015). On Bayesian estimation of marginal structural models. Biometrics, 71(2), 279-288.	Kleibergen, F., & Zivot, E. (2003). Bayesian and classical approaches to instrumental variable regression. Journal of Econometrics, 114(1), 29-72.
Alias	Bayesian MSM, Bayesian MSM-IPW, Bayesian weighted structural model, Bayesian causal MSM	Bayesian IV, Bayesian 2SLS, Bayesian LIML, BayesIV
Apparentées	6	6
Résumé	Bayesian Marginal Structural Model (Bayesian MSM) combines the causal identification power of inverse-probability-weighted marginal structural models with Bayesian posterior inference. Rather than relying on point estimates and asymptotic standard errors, it propagates uncertainty through a full posterior distribution over causal effect parameters, offering coherent uncertainty quantification for causal effects of time-varying treatments.	Bayesian Instrumental Variables combines the instrumental variable strategy for addressing endogeneity with Bayesian posterior inference. Instead of relying on asymptotic sampling distributions, it places prior distributions over all structural parameters and recovers a full posterior distribution for the causal effect, providing probability statements about the parameter rather than p-values — especially valuable when instruments are weak or the sample is small.