Comparar métodos

	Diseño de Discontinuidad de Regresión Difusa Multiperiódica	Diseño difuso de discontinuidad de regresión
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2001 (fuzzy RD); multi-period extension ~2010s	2001
Autor original	Hahn, Todd & Van der Klaauw (foundational fuzzy RD, 2001); extended to multi-period settings by Cattaneo, Idrobo & Titiunik and subsequent applied literature	Hahn, Todd & van der Klaauw
Tipo	Quasi-experimental causal inference	Quasi-experimental causal inference
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.	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	multi-period fuzzy RDD, fuzzy RD with repeated assignment, multi-wave fuzzy RD, staggered fuzzy RDD	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Relacionados	4	5
Resumen	Multi-period fuzzy regression discontinuity design estimates a local average treatment effect when a cutoff rule only partially determines treatment — that is, crossing the threshold raises the probability of treatment but does not guarantee it — and when this assignment process is observed across two or more time periods or cohorts, enabling pooled or period-specific causal estimates under repeated near-threshold comparisons.	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.