Methoden vergelijken

	Bayesiaanse Coarsened Exact Matching	Bayesian Matching Estimator
Vakgebied	Causale inferentie	Causale inferentie
Familie	Regression model	Regression model
Jaar van ontstaan	2011-2012	1978–1998
Grondlegger	Iacus, King & Porro (CEM framework, 2012); Bayesian extensions by Hill and subsequent authors	Donald B. Rubin (Bayesian causal framework); extended by Heckman, Ichimura & Todd (matching estimator formalization)
Type	Quasi-experimental matching with Bayesian inference	Bayesian causal inference / nonparametric matching
Oorspronkelijke bron	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. The Annals of Statistics, 6(1), 34-58.
Aliassen	Bayesian CEM, BCEM, Bayesian monotonic imbalance bounding matching	Bayesian matching, Bayesian nonparametric matching, Bayes-ATE matching, posterior matching estimator
Verwant	6	6
Samenvatting	Bayesian Coarsened Exact Matching (Bayesian CEM) combines the coarsening-and-exact-matching framework of Iacus, King, and Porro with Bayesian posterior inference. Covariates are discretised into coarser bins so that treated and control units can be matched exactly within those bins, and Bayesian priors are then placed on the treatment-effect parameters to produce full posterior distributions over the causal estimand rather than a single point estimate.	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.