Compară metode

	Estimator de potrivire spațială	Designul de Regresie cu Discontinuitate Spațială (RDD Spațial)
Domeniu	Inferență cauzală	Inferență cauzală
Familie	Regression model	Regression model
Anul apariției	2000s–2010s	2010s
Autorul original	Extension of Abadie & Imbens (2006) matching estimator to spatial settings; geographic applications developed in urban/environmental econometrics literature	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)
Tip	Quasi-experimental causal inference	Quasi-experimental causal inference
Sursa seminală	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.	Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.
Denumiri alternative	geographic matching estimator, spatial nearest-neighbor matching, location-based matching estimator, spatially-weighted matching	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Înrudite	6	4
Rezumat	The Spatial Matching Estimator estimates causal treatment effects by pairing each treated geographic unit with one or more similar untreated units nearby, exploiting the assumption that units close in space share similar unobserved characteristics. By restricting matches to a geographic neighbourhood or weighting by spatial proximity, the method controls for location-specific confounders that standard matching ignores.	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.