Methoden vergleichen

	Bayesian Matching Estimator	Matching-Schätzer
Fachgebiet	Kausale Inferenz	Kausale Inferenz
Familie	Regression model	Regression model
Entstehungsjahr	1978–1998	1973
Urheber	Donald B. Rubin (Bayesian causal framework); extended by Heckman, Ichimura & Todd (matching estimator formalization)	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Typ	Bayesian causal inference / nonparametric matching	Nonparametric matching / causal inference
Wegweisende Quelle	Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. The Annals of Statistics, 6(1), 34-58.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Aliasnamen	Bayesian matching, Bayesian nonparametric matching, Bayes-ATE matching, posterior matching estimator	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Verwandt	6	6
Zusammenfassung	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.	The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome.