Salīdzināt metodes

	Telpiskās regresijas diskontinuitātes dizains (Spatial RDD)	Fuzzy Regression Discontinuity Design
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2010s	2001
Autors	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)	Hahn, Todd & van der Klaauw
Tips	Quasi-experimental causal inference	Quasi-experimental causal inference
Pirmavots	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.
Citi nosaukumi	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Saistītās	4	5
Kopsavilkums	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.