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Mètode del Control Sintètic (SCM)×Mètodes de parellatge (CEM / Òptim / Genètic)×
CampInferència causalInferència causal
FamíliaRegression modelRegression model
Any d'origen20102012
Autor originalAbadie, Diamond & HainmuellerIacus, King & Porro (CEM); Hansen (optimal/full matching)
TipusCounterfactual causal-inference modelMatching for causal inference
Font 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 ↗
Àliessynthetic control method, SCM, synthetic counterfactual, Sentetik Kontrol Yöntemi (SCM)coarsened exact matching, optimal matching, genetic matching, CEM
Relacionats55
ResumThe 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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ScholarGateCompara mètodes: Synthetic Control · Matching Methods. Recuperat el 2026-06-17 de https://scholargate.app/ca/compare