Comparar métodos

	Emparejamiento Bayesiano por Puntuación de Propensión	Estimación Doblemente Robusta (AIPW)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2012	2005
Autor original	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)	Robins & Rotnitzky; Bang & Robins
Tipo	Bayesian causal inference / matching	Semiparametric causal estimator
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.	Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129.
Alias	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
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.	Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified.