Comparar métodos

	Modelo Estrutural Marginal Bayesiano	Variáveis Instrumentais Bayesianas (IV Bayesiana)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	2015 (Bayesian extension); 2000 (MSM foundation)	2003
Autor original	Saarela, Stephens, Moodie & Klein (Bayesian extension); Robins, Hernan & Brumback (original MSM)	Kleibergen & Zivot (2003); Lancaster (2004)
Tipo	Causal inference / Bayesian weighted regression	Causal inference / Bayesian estimation
Fonte seminal	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.
Outros nomes	Bayesian MSM, Bayesian MSM-IPW, Bayesian weighted structural model, Bayesian causal MSM	Bayesian IV, Bayesian 2SLS, Bayesian LIML, BayesIV
Relacionados	6	6
Resumo	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.