ScholarGate
Asistent

Porovnat metody

Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.

Metody párování (CEM / optimální / genetické)×Lokální průměrný účinek léčby (LATE / CACE)×
OborKauzální inferenceKauzální inference
RodinaRegression modelRegression model
Rok vzniku20121994
TvůrceIacus, King & Porro (CEM); Hansen (optimal/full matching)Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)
TypMatching for causal inferenceInstrumental-variable causal estimand
Původní zdrojIacus, 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 ↗
Další názvycoarsened exact matching, optimal matching, genetic matching, CEMLATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE)
Příbuzné55
ShrnutíMatching 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.
ScholarGateDatová sada
  1. v1
  2. 2 Zdroje
  3. PUBLISHED
  1. v1
  2. 2 Zdroje
  3. PUBLISHED

Přejít na hledání Stáhnout prezentaci

ScholarGatePorovnat metody: Matching Methods · Local Average Treatment Effect. Získáno 2026-06-17 z https://scholargate.app/cs/compare