Sammenlign metoder

	Spatial Regression Discontinuity Design (Spatial RDD)	Fuzzy Regression Discontinuity Design
Fagområde	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model
Oprindelsesår	2010s	2001
Ophavsperson	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)	Hahn, Todd & van der Klaauw
Type	Quasi-experimental causal inference	Quasi-experimental causal inference
Oprindelig kilde	Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.	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.
Aliasser	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Relaterede	4	5
Resumé	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.	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.