Comparar métodos

	Entropía Bayesiana de Balanceo	Ponderación por Probabilidad Inversa de Tratamiento (IPW / IPTW)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2012-2020s	2000
Autor original	Hainmueller (2012, entropy balancing foundation); Bayesian extension developed in subsequent causal inference literature	Robins, Hernán & Brumback
Tipo	Weighting-based causal estimator with Bayesian uncertainty quantification	Causal inference weighting estimator
Fuente seminal	Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Alias	BEB, Bayesian EB, Bayesian covariate balancing, entropy balancing with Bayesian inference	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Relacionados	6	5
Resumen	Bayesian Entropy Balancing extends the classical entropy balancing approach — which reweights control units so that their covariate moments match the treated group exactly — by embedding this reweighting within a Bayesian framework. This allows researchers to incorporate prior beliefs about treatment propensities, propagate parameter uncertainty into the final causal estimate, and obtain credible intervals rather than only classical confidence intervals.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.