Salīdzināt metodes

	Marginal Structural Model (MSM)	Aproksimēta novērtēšana (PSW / IPW)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2000	1983 (propensity score); 2003 (efficient IPW estimator)
Autors	James M. Robins, Miguel A. Hernan, Babette Brumback	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Tips	Causal model / semiparametric weighting	Causal inference / reweighting
Pirmavots	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	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	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model	PSW, inverse probability weighting, IPW, propensity-based weighting
Saistītās	5	6
Kopsavilkums	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.	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).