Comparar métodos

	Coarsened Exact Matching Bayesiano	Propensity Score Matching
Área	Inferência causal	Estatística para pesquisa
Família	Regression model	Process / pipeline
Ano de origem	2011-2012	1983
Autor original	Iacus, King & Porro (CEM framework, 2012); Bayesian extensions by Hill and subsequent authors	Paul Rosenbaum and Donald Rubin
Tipo	Quasi-experimental matching with Bayesian inference	Method
Fonte seminal	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Outros nomes	Bayesian CEM, BCEM, Bayesian monotonic imbalance bounding matching	PSM, propensity score weighting, covariate balance
Relacionados	6	3
Resumo	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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.