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Lokální průměrný účinek léčby (LATE / CACE)×Regresní diskontinuitní design (RDD)×
OborKauzální inferenceKauzální inference
RodinaRegression modelRegression model
Rok vzniku19942008
TvůrceImbens & Angrist (1994); Angrist, Imbens & Rubin (1996)Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
TypInstrumental-variable causal estimandQuasi-experimental causal design
Původní zdrojImbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475. DOI ↗Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗
Další názvyLATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE)RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Příbuzné55
ShrnutíThe 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.Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.
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ScholarGatePorovnat metody: Local Average Treatment Effect · Regression Discontinuity. Získáno 2026-06-18 z https://scholargate.app/cs/compare