Porovnat metody

	Marginal Structural Model ve výzkumu vzdělávání	Metoda instrumentálních proměnných (IV) pro kauzální inferenci
Obor	Kauzální inference	Ekonomika zdravotnictví
Rodina	Regression model	Process / pipeline
Rok vzniku	2000 (method); 2006 (canonical education application)	1990s (modern applications)
Tvůrce	James M. Robins, Miguel A. Hernán, Babette Brumback (epidemiology); Guanglei Hong & Stephen Raudenbush (education application)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Typ	Causal inference / weighted regression model	Method
Původní zdroj	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Další názvy	MSM, marginal structural model, MSM with inverse probability weighting, IPW-MSM	IV, two-stage least squares, TSLS, causal estimation
Příbuzné	5	3
Shrnutí	A marginal structural model (MSM) is a causal inference technique that uses inverse probability weighting to estimate the effect of a treatment or educational intervention that changes over time. Introduced by Robins, Hernán and Brumback (2000) in epidemiology and brought into education by Hong and Raudenbush (2006), MSMs handle time-varying confounding — a challenge that conventional regression cannot resolve.	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.