Comparer des méthodes

	Estimateur par Appariement Spatial	Discontinuité de régression spatiale (Spatial RDD)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2000s–2010s	2010s
Auteur d'origine	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)
Type	Quasi-experimental causal inference	Quasi-experimental causal inference
Source fondatrice	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.
Alias	geographic matching estimator, spatial nearest-neighbor matching, location-based matching estimator, spatially-weighted matching	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Apparentées	6	4
Résumé	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.