Сравнение методов

	Байесовское укрупненное точное согласование	Укрупненное точное сопоставление (CEM)
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2011-2012	2011-2012
Автор метода	Iacus, King & Porro (CEM framework, 2012); Bayesian extensions by Hill and subsequent authors	Iacus, King, & Porro
Тип	Quasi-experimental matching with Bayesian inference	Matching / causal inference
Основополагающий источник	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Другие названия	Bayesian CEM, BCEM, Bayesian monotonic imbalance bounding matching	CEM, coarsened matching, monotonic imbalance bounding matching
Связанные	6	6
Сводка	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.	Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on each covariate independently, yielding a matched sample on which any estimator can be applied without relying on a propensity score model.