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Método de Controle Sintético (SCM)×Métodos de Pareamento (CEM / Ótimo / Genético)×
ÁreaInferência causalInferência causal
FamíliaRegression modelRegression model
Ano de origem20102012
Autor originalAbadie, Diamond & HainmuellerIacus, King & Porro (CEM); Hansen (optimal/full matching)
TipoCounterfactual causal-inference modelMatching for causal inference
Fonte seminalAbadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505. DOI ↗Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗
Outros nomessynthetic control method, SCM, synthetic counterfactual, Sentetik Kontrol Yöntemi (SCM)coarsened exact matching, optimal matching, genetic matching, CEM
Relacionados55
ResumoThe Synthetic Control Method, introduced by Abadie, Diamond and Hainmueller in 2010, builds a weighted counterfactual for a single treated unit from a pool of untreated donor units. It is widely regarded as the gold standard for evaluating large policy interventions, natural experiments, and N=1 case studies where no obvious comparison unit exists.Matching Methods are a family of causal-inference techniques beyond propensity-score matching that pair treated and control units with similar covariates so that a treatment effect can be read off the balanced sample. The family includes Coarsened Exact Matching (Iacus, King & Porro, 2012), optimal matching, and genetic matching.
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ScholarGateComparar métodos: Synthetic Control · Matching Methods. Recuperado em 2026-06-17 de https://scholargate.app/pt/compare