Comparar métodos

	Modelo Estructural Marginal en Investigación Educativa	Diseño de Regresión Discontinua (RDD)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2000 (method); 2006 (canonical education application)	2008
Autor original	James M. Robins, Miguel A. Hernán, Babette Brumback (epidemiology); Guanglei Hong & Stephen Raudenbush (education application)	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Tipo	Causal inference / weighted regression model	Quasi-experimental causal design
Fuente seminal	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Alias	MSM, marginal structural model, MSM with inverse probability weighting, IPW-MSM	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Relacionados	5	5
Resumen	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.	Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.