Σύγκριση μεθόδων

	Στάθμιση Βαθμολογίας Τάσης (Bayesian Propensity Score Weighting)	Οριακό Δομικό Μοντέλο (Marginal Structural Model - MSM)
Πεδίο	Αιτιακή Συμπερασματολογία	Αιτιακή Συμπερασματολογία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2009	2000
Δημιουργός	McCandless, Gustafson & Austin	James M. Robins, Miguel A. Hernan, Babette Brumback
Τύπος	Bayesian causal weighting estimator	Causal model / semiparametric weighting
Θεμελιώδης πηγή	McCandless, L. C., Gustafson, P., & Austin, P. C. (2009). Bayesian propensity score analysis for observational data. Statistics in Medicine, 28(1), 94–112.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Εναλλακτικές ονομασίες	Bayesian PSW, Bayesian IPW, Bayesian inverse probability weighting, Bayesian propensity weighting	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Συναφείς	6	5
Σύνοψη	Bayesian Propensity Score Weighting estimates causal treatment effects in observational data by combining a Bayesian model for the propensity score with inverse probability weighting. By placing a prior over propensity-score parameters and propagating posterior uncertainty through the weighting step, this approach yields fully probabilistic uncertainty intervals for the average treatment effect, accounting for the uncertainty in both the score model and the outcome.	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.