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	Fuzzy Regression Discontinuity for Policy Evaluation	Politikevaluering Regressionsdiskontinuitetsdesign
Fagområde	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model
Oprindelsesår	2001	1960; policy evaluation applications widespread from 2000s
Ophavsperson	Hahn, Todd & Van der Klaauw	Thistlethwaite & Campbell (1960); popularized in policy evaluation by Lee & Lemieux (2010)
Type	Quasi-experimental / local IV estimator	Quasi-experimental causal design
Oprindelig kilde	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.	Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.
Aliasser	Fuzzy RDD, Fuzzy RD, Fuzzy Regression Discontinuity, Imperfect Compliance RDD	Policy RDD, RD design in policy evaluation, regression discontinuity policy analysis, RDD policy impact
Relaterede	5	5
Resumé	Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates the causal effect of a policy when eligibility is determined by crossing a threshold on a continuous score, but actual take-up or compliance is imperfect. Developed formally by Hahn, Todd, and Van der Klaauw (2001), it uses the threshold as an instrumental variable to recover a Local Average Treatment Effect (LATE) among compliers near the cutoff.	Policy 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.