Comparar métodos

	Diseño de Regresión Discontinua Robusta	Diseño difuso de discontinuidad de regresión
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2014	2001
Autor original	Calonico, Cattaneo & Titiunik	Hahn, Todd & van der Klaauw
Tipo	Quasi-experimental causal inference	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 RDD, Bias-corrected RDD, CCT estimator, rdrobust	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Relacionados	4	5
Resumen	Robust RDD extends the classical regression discontinuity design with bias correction and robust confidence intervals, addressing the under-coverage problem of conventional RDD inference. Developed by Calonico, Cattaneo, and Titiunik (2014), it uses local polynomial estimation with a bias-corrected point estimate and a wider variance term that accounts for the added uncertainty, yielding confidence intervals with correct asymptotic coverage.	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.