Comparar métodos

	Heterogeneous Treatment Effect Inverse Probability Weighting	Ponderación por Puntuación de Propensión (PSW / IPW)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2003–2015	1983 (propensity score); 2003 (efficient IPW estimator)
Autor original	Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Tipo	Causal inference / weighted regression	Causal inference / reweighting
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.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.
Alias	HTE-IPW, CATE-IPW, heterogeneous IPW, conditional effect IPW	PSW, inverse probability weighting, IPW, propensity-based weighting
Relacionados	5	6
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.	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).