Comparar métodos

	Diseño de Discontinuidad por Regresión Difusa Robusta	Diseño difuso de discontinuidad de regresión
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2014 (robust CCT estimator); 2001 (fuzzy RDD formalization)	2001
Autor original	Calonico, Cattaneo, and Titiunik (robust inference framework); Hahn, Todd, and Van der Klaauw (fuzzy RDD formalization)	Hahn, Todd & van der Klaauw
Tipo	Quasi-experimental causal inference with IV at threshold	Quasi-experimental causal inference
Fuente seminal	Calonico, S., Cattaneo, M. D., & Titiunik, R. (2014). Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica, 82(6), 2295-2326.	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.
Alias	Robust Fuzzy RDD, Fuzzy RD with robust inference, bias-corrected fuzzy RD, CCT fuzzy RDD	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Relacionados	5	5
Resumen	Robust Fuzzy Regression Discontinuity Design estimates a local average treatment effect (LATE) at a threshold where crossing the cutoff raises — but does not guarantee — treatment receipt. Introduced by Calonico, Cattaneo, and Titiunik (2014), the robust framework applies bias-corrected local polynomial estimation with a robust variance estimator, correcting the coverage failures of conventional bandwidth-optimal inference in both the sharp and fuzzy cases.	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.