Comparar métodos

	Emparejamiento Bayesiano por Puntuación de Propensión	Diferencias en Diferencias Bayesiano
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2012	2015-2023
Autor original	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)	Li & Marchand (formal Bayesian DiD framework); Brodersen et al. (Bayesian causal inference in time series)
Tipo	Bayesian causal inference / matching	Bayesian causal inference / panel regression
Fuente seminal	Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609.	Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529.
Alias	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting	Bayesian DiD, Bayes DiD, Bayesian diff-in-diff, Bayesian panel causal estimator
Relacionados	6	5
Resumen	Bayesian Propensity Score Matching (Bayesian PSM) extends classical propensity score matching by placing a prior distribution over the propensity model parameters and propagating posterior uncertainty through the matching and outcome stages. Introduced formally by Kaplan and Chen (2012), it offers a principled account of estimation uncertainty that frequentist matching commonly ignores, and allows incorporation of substantive prior knowledge about treatment selection.	Bayesian Difference-in-Differences applies Bayesian statistical inference to the classic DiD design, replacing frequentist point estimates with full posterior distributions over the treatment effect. This yields not only an estimate of the causal effect but also a coherent probability statement about its magnitude and uncertainty, making it especially useful when sample sizes are modest or informative prior knowledge is available.