Comparar métodos

	Estimador de Emparejamiento de Efectos Heterogéneos del Tratamiento	Emparejamiento por Puntuación de Propensión
Campo	Inferencia causal	Estadística para la investigación
Familia	Regression model	Process / pipeline
Año de origen	1997-2006	1983
Autor original	Heckman, Ichimura & Todd; Abadie & Imbens	Paul Rosenbaum and Donald Rubin
Tipo	Causal inference / nonparametric matching	Method
Fuente seminal	Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme. Review of Economic Studies, 64(4), 605-654.	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 matching, subgroup matching estimator, conditional matching estimator, CATE matching	PSM, propensity score weighting, covariate balance
Relacionados	6	3
Resumen	The Heterogeneous Treatment Effect (HTE) Matching Estimator extends standard matching to recover how treatment impacts differ across subgroups or covariate values. Rather than reporting a single average treatment effect, it pairs treated and control units on observed characteristics and then estimates the conditional average treatment effect (CATE) as a function of those characteristics — revealing who benefits most, least, or not at all.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.