Comparar métodos

	Regresión Fuzzosa por Discontinuidad para Evaluación de Políticas	Evaluación de Políticas Diseño por Regresión Discontinua
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2001	1960; policy evaluation applications widespread from 2000s
Autor original	Hahn, Todd & Van der Klaauw	Thistlethwaite & Campbell (1960); popularized in policy evaluation by Lee & Lemieux (2010)
Tipo	Quasi-experimental / local IV estimator	Quasi-experimental causal design
Fuente seminal	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.
Alias	Fuzzy RDD, Fuzzy RD, Fuzzy Regression Discontinuity, Imperfect Compliance RDD	Policy RDD, RD design in policy evaluation, regression discontinuity policy analysis, RDD policy impact
Relacionados	5	5
Resumen	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.