Confronta i metodi

	Modello Strutturale Marginale nella Ricerca Educativa	Metodo delle Variabili Strumentali (IV) per l'Inferenza Causale
Campo	Inferenza causale	Economia sanitaria
Famiglia	Regression model	Process / pipeline
Anno di origine	2000 (method); 2006 (canonical education application)	1990s (modern applications)
Ideatore	James M. Robins, Miguel A. Hernán, Babette Brumback (epidemiology); Guanglei Hong & Stephen Raudenbush (education application)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tipo	Causal inference / weighted regression model	Method
Fonte seminale	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.
Alias	MSM, marginal structural model, MSM with inverse probability weighting, IPW-MSM	IV, two-stage least squares, TSLS, causal estimation
Correlati	5	3
Sintesi	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.