Comparar métodos

	Estimador Bayesiano de Apareamiento	Emparejamiento por Puntuación de Propensión
Campo	Inferencia causal	Estadística para la investigación
Familia	Regression model	Process / pipeline
Año de origen	1978–1998	1983
Autor original	Donald B. Rubin (Bayesian causal framework); extended by Heckman, Ichimura & Todd (matching estimator formalization)	Paul Rosenbaum and Donald Rubin
Tipo	Bayesian causal inference / nonparametric matching	Method
Fuente seminal	Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. The Annals of Statistics, 6(1), 34-58.	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	Bayesian matching, Bayesian nonparametric matching, Bayes-ATE matching, posterior matching estimator	PSM, propensity score weighting, covariate balance
Relacionados	6	3
Resumen	The Bayesian Matching Estimator estimates average treatment effects in observational studies by combining classical nearest-neighbour or kernel matching with a Bayesian posterior over the treatment effect. It inherits matching's covariate-balancing logic while propagating uncertainty through a full posterior distribution rather than relying on asymptotic standard errors, yielding credible intervals that reflect both sampling variability and prior knowledge.	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.