Σύγκριση μεθόδων

	Μπεϋζιανό Δομικό Οριακό Μοντέλο	Αντίστροφη Πιθανότητα Στάθμισης Θεραπείας (IPW / IPTW)
Πεδίο	Αιτιακή Συμπερασματολογία	Αιτιακή Συμπερασματολογία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2015 (Bayesian extension); 2000 (MSM foundation)	2000
Δημιουργός	Saarela, Stephens, Moodie & Klein (Bayesian extension); Robins, Hernan & Brumback (original MSM)	Robins, Hernán & Brumback
Τύπος	Causal inference / Bayesian weighted regression	Causal inference weighting estimator
Θεμελιώδης πηγή	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.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Εναλλακτικές ονομασίες	Bayesian MSM, Bayesian MSM-IPW, Bayesian weighted structural model, Bayesian causal MSM	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Συναφείς	6	5
Σύνοψη	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.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.