Comparar métodos

	Diseño de Discontinuidad de Regresión Difusa Espacial	Diseño de Discontinuidad por Regresión Espacial (Spatial RDD)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2015	2010s
Autor original	Keele & Titiunik (2015); fuzzy extension of geographic RDD building on Imbens & Lemieux (2008)	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)
Tipo	Quasi-experimental causal inference / IV-based spatial design	Quasi-experimental causal inference
Fuente seminal	Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155.	Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.
Alias	Spatial Fuzzy RD, Geographic Fuzzy RDD, Spatial Fuzzy RDD, Geo-Fuzzy RD	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Relacionados	5	4
Resumen	Spatial Fuzzy Regression Discontinuity Design (Spatial Fuzzy RDD) estimates a local average treatment effect when a geographic boundary determines treatment eligibility but some units on either side of the boundary fail to comply with their assigned status. It combines the spatial running-variable logic of geographic RDD with the instrumental-variable correction for imperfect compliance used in fuzzy RDD.	Spatial Regression Discontinuity Design uses a geographic or administrative boundary as the threshold that assigns units to treatment. Observations just inside one side of the boundary are compared with those just outside it, exploiting the near-random variation in treatment status near the cutoff to recover a local causal effect. The approach is widely used in economics, political science, and public health when policies or institutions change sharply at a border.