Comparar métodos

	Heterogeneous Treatment Effect Inverse Probability Weighting	Modelo Estructural Marginal (MSM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2003–2015	2000
Autor original	Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli	James M. Robins, Miguel A. Hernan, Babette Brumback
Tipo	Causal inference / weighted regression	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	HTE-IPW, CATE-IPW, heterogeneous IPW, conditional effect IPW	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Relacionados	5	5
Resumen	HTE-IPW extends standard inverse probability weighting to recover how causal effects vary across subgroups or covariate values. By reweighting each observation by the inverse of its estimated treatment probability, the method creates a pseudo-population in which treatment is independent of background characteristics, and then estimates conditional average treatment effects (CATEs) as a function of those characteristics.	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.