Salīdzināt metodes

	Telpiskā izplūdušās regresijas diskontinuitātes metode	Telpiskās regresijas diskontinuitātes dizains (Spatial RDD)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2015	2010s
Autors	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)
Tips	Quasi-experimental causal inference / IV-based spatial design	Quasi-experimental causal inference
Pirmavots	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.
Citi nosaukumi	Spatial Fuzzy RD, Geographic Fuzzy RDD, Spatial Fuzzy RDD, Geo-Fuzzy RD	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Saistītās	5	4
Kopsavilkums	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.