Compară metode

	Estimator de potrivire spațială	Estimator de potrivire
Domeniu	Inferență cauzală	Inferență cauzală
Familie	Regression model	Regression model
Anul apariției	2000s–2010s	1973
Autorul original	Extension of Abadie & Imbens (2006) matching estimator to spatial settings; geographic applications developed in urban/environmental econometrics literature	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Tip	Quasi-experimental causal inference	Nonparametric matching / 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.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Denumiri alternative	geographic matching estimator, spatial nearest-neighbor matching, location-based matching estimator, spatially-weighted matching	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Înrudite	6	6
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.	The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome.