Salīdzināt metodes

	Bayesiskais marginālais strukturālais modelis	Aproksimēta novērtēšana (PSW / IPW)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2015 (Bayesian extension); 2000 (MSM foundation)	1983 (propensity score); 2003 (efficient IPW estimator)
Autors	Saarela, Stephens, Moodie & Klein (Bayesian extension); Robins, Hernan & Brumback (original MSM)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Tips	Causal inference / Bayesian weighted regression	Causal inference / reweighting
Pirmavots	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.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.
Citi nosaukumi	Bayesian MSM, Bayesian MSM-IPW, Bayesian weighted structural model, Bayesian causal MSM	PSW, inverse probability weighting, IPW, propensity-based weighting
Saistītās	6	6
Kopsavilkums	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.	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).