Σύγκριση μεθόδων

	Μπεϋζιανή Στάθμιση Αντίστροφης Πιθανότητας	Οριακό Δομικό Μοντέλο (Marginal Structural Model - MSM)
Πεδίο	Αιτιακή Συμπερασματολογία	Αιτιακή Συμπερασματολογία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2015	2000
Δημιουργός	Saarela, Stephens, Moodie & Klein (2015); Liao & Zigler (2020)	James M. Robins, Miguel A. Hernan, Babette Brumback
Τύπος	Bayesian causal weighting estimator	Causal model / semiparametric weighting
Θεμελιώδης πηγή	Saarela, O., Stephens, D. A., Moodie, E. E. M., & Klein, M. B. (2015). On risk prediction and characterisation of treatment effects in a Bayesian framework using the propensity score. Statistics in Medicine, 34(14), 2170-2185.	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 IPW, BIPW, Bayesian propensity-weighted estimation, Bayesian marginal structural weighting	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Συναφείς	6	5
Σύνοψη	Bayesian Inverse Probability Weighting (Bayesian IPW) extends the classical IPW estimator by placing prior distributions over the propensity-score model parameters and propagating that uncertainty into the causal-effect estimate. The result is a posterior distribution for the average treatment effect that fully accounts for both propensity-score estimation uncertainty and outcome-model uncertainty, enabling credible-interval inference rather than relying on asymptotic approximations.	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.