Regression modelQuasi-experimental / causal inference

Spatial Regression Discontinuity Design (Spatial 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.

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Sources

  1. Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903. DOI: 10.3982/ECTA8121
  2. Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155. DOI: 10.1093/pan/mpu014

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Referenced by

ScholarGateSpatial Regression Discontinuity Design (Spatial Regression Discontinuity Design). Retrieved 2026-06-04 from https://scholargate.app/tr/causal-inference/spatial-regression-discontinuity-design