Сравнение методов

	Робастный регрессионный дизайн разрыва (Robust Regression Discontinuity Design)	Нечеткий регрессионный разрывный дизайн
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2014	2001
Автор метода	Calonico, Cattaneo & Titiunik	Hahn, Todd & van der Klaauw
Тип	Quasi-experimental causal inference	Quasi-experimental causal inference
Основополагающий источник	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.
Другие названия	Robust RDD, Bias-corrected RDD, CCT estimator, rdrobust	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Связанные	4	5
Сводка	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.