Methoden vergleichen

	Robuste Regressions-Diskontinuitäts-Analyse (Robust RDD)	Fuzzy Regression Discontinuity Design
Fachgebiet	Kausale Inferenz	Kausale Inferenz
Familie	Regression model	Regression model
Entstehungsjahr	2014	2001
Urheber	Calonico, Cattaneo & Titiunik	Hahn, Todd & van der Klaauw
Typ	Quasi-experimental causal inference	Quasi-experimental causal inference
Wegweisende Quelle	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.
Aliasnamen	Robust RDD, Bias-corrected RDD, CCT estimator, rdrobust	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Verwandt	4	5
Zusammenfassung	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.