Comparar métodos

	Estimación Bayesiana Doblemente Robusta	Modelo Estructural Marginal (MSM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2005–2010s	2000
Autor original	Bang & Robins (2005); Bayesian extensions by Scharfstein, Kennedy, and others	James M. Robins, Miguel A. Hernan, Babette Brumback
Tipo	Semiparametric causal estimation with Bayesian inference	Causal model / semiparametric weighting
Fuente seminal	Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Alias	Bayesian DR, Bayesian AIPW, Bayesian augmented inverse probability weighting, Bayesian semiparametric causal estimation	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Relacionados	5	5
Resumen	Bayesian Doubly Robust Estimation combines the classical doubly robust (DR) augmented inverse probability weighting framework with Bayesian inference. It simultaneously models the propensity score and the outcome regression, placing prior distributions over both, and derives a posterior distribution over the average treatment effect that remains consistent even if one of the two component models is misspecified.	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.