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Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.

Lokale Gemiddelde Behandelingsuitkomst (LATE / CACE)×Regressiediscontinuïteitsontwerp (RDD)×
VakgebiedCausale inferentieCausale inferentie
FamilieRegression modelRegression model
Jaar van ontstaan19942008
GrondleggerImbens & Angrist (1994); Angrist, Imbens & Rubin (1996)Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
TypeInstrumental-variable causal estimandQuasi-experimental causal design
Oorspronkelijke bronImbens, 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 ↗
AliassenLATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE)RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Verwant55
SamenvattingThe 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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ScholarGateMethoden vergelijken: Local Average Treatment Effect · Regression Discontinuity. Geraadpleegd op 2026-06-18 via https://scholargate.app/nl/compare