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Méthodes de jumelage (CEM / Optimal / Génétique)×Effet Traitement Moyen Local (ETML / CACE)×
DomaineInférence causaleInférence causale
FamilleRegression modelRegression model
Année d'origine20121994
Auteur d'origineIacus, King & Porro (CEM); Hansen (optimal/full matching)Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)
TypeMatching for causal inferenceInstrumental-variable causal estimand
Source fondatriceIacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475. DOI ↗
Aliascoarsened exact matching, optimal matching, genetic matching, CEMLATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE)
Apparentées55
RésuméMatching 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.The Local Average Treatment Effect is an instrumental-variable estimand, introduced by Imbens and Angrist (1994) and formalised with Rubin (1996), that recovers the average treatment effect for the subpopulation of compliers — units whose treatment status is actually moved by the instrument. It is closely tied to compliance analysis.
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ScholarGateComparer des méthodes: Matching Methods · Local Average Treatment Effect. Consulté le 2026-06-17 sur https://scholargate.app/fr/compare