Comparar métodos

	Ponderación por Puntuación de Propensión en Datos de Panel	Modelo Estructural Marginal (MSM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2000-2003	2000
Autor original	Hirano, Imbens & Ridder; Robins, Hernan & Brumback	James M. Robins, Miguel A. Hernan, Babette Brumback
Tipo	Causal inference / panel weighting	Causal model / semiparametric weighting
Fuente seminal	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Alias	panel PSW, panel IPW, longitudinal propensity score weighting, panel inverse probability weighting	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Relacionados	5	5
Resumen	Panel Data Propensity Score Weighting (panel PSW) extends inverse probability weighting to longitudinal settings where the same units are observed across multiple time periods. It reweights observations by the inverse of each unit's time-varying probability of receiving treatment, creating a pseudo-population in which treatment is balanced on observed covariates at each period, and then estimates causal effects from repeated-measures data.	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.