Comparar métodos

	Análisis de Sensibilidad Bayesiano para la Causalidad	Modelo Estructural Marginal (MSM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2000s–2010s	2000
Autor original	McCandless, Gustafson & Austin (2007); Gustafson (2015)	James M. Robins, Miguel A. Hernan, Babette Brumback
Tipo	Bayesian causal sensitivity analysis	Causal model / semiparametric weighting
Fuente seminal	McCandless, L. C., Gustafson, P., & Austin, P. C. (2007). Bayesian propensity score analysis for observational data. Statistics in Medicine, 26(8), 1704-1718.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Alias	Bayesian sensitivity analysis, Bayesian bias analysis, probabilistic sensitivity analysis for confounding, Bayesian unmeasured confounding analysis	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Relacionados	6	5
Resumen	Bayesian sensitivity analysis for causality quantifies how much an unmeasured confounder would need to influence both treatment assignment and outcome to overturn a causal conclusion. Rather than testing a single worst-case scenario, it places prior distributions over the strength of hidden confounding, propagates uncertainty through a full Bayesian model, and reports a posterior distribution for the causal effect that honestly reflects what is and is not identified from observed data.	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.