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Regresjonsdiskontinuitetsdesign for politikkevaluering×Fuzzy Regression Discontinuity Design×
FagfeltKausal inferensKausal inferens
FamilieRegression modelRegression model
Opprinnelsesår1960; policy evaluation applications widespread from 2000s2001
OpphavspersonThistlethwaite & Campbell (1960); popularized in policy evaluation by Lee & Lemieux (2010)Hahn, Todd & van der Klaauw
TypeQuasi-experimental causal designQuasi-experimental causal inference
Opprinnelig kildeLee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355. DOI ↗Hahn, J., Todd, P., & van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209. DOI ↗
AliasPolicy RDD, RD design in policy evaluation, regression discontinuity policy analysis, RDD policy impactFuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Relaterte55
SammendragPolicy Evaluation Regression Discontinuity Design (Policy RDD) exploits a known eligibility threshold in a policy rule to estimate the causal effect of that policy on outcomes. Units just below the cutoff serve as a credible comparison group for units just above it, making RDD one of the most transparent quasi-experimental strategies for assessing what a policy actually achieves.Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates causal effects when eligibility for a treatment is determined by a threshold on a running variable but actual take-up of that treatment is imperfect — some eligible units do not receive treatment and some ineligible units do. The cutoff acts as an instrument, and the estimand is a Local Average Treatment Effect (LATE) for compliers near the threshold.
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ScholarGateSammenlign metoder: Policy Evaluation Regression Discontinuity Design · Fuzzy Regression Discontinuity. Hentet 2026-06-19 fra https://scholargate.app/no/compare