Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèle structurel marginal dans la recherche en éducation× | Méthode des variables instrumentales (VI) pour l'inférence causale× | |
|---|---|---|
| Domaine≠ | Inférence causale | Économie de la santé |
| Famille≠ | Regression model | Process / pipeline |
| Année d'origine≠ | 2000 (method); 2006 (canonical education application) | 1990s (modern applications) |
| Auteur d'origine≠ | James M. Robins, Miguel A. Hernán, Babette Brumback (epidemiology); Guanglei Hong & Stephen Raudenbush (education application) | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Type≠ | Causal inference / weighted regression model | Method |
| Source fondatrice≠ | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| Alias | MSM, marginal structural model, MSM with inverse probability weighting, IPW-MSM | IV, two-stage least squares, TSLS, causal estimation |
| Apparentées≠ | 5 | 3 |
| Résumé≠ | 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. |
| ScholarGateJeu de données ↗ |
|
|