Salīdzināt metodes

	Novērtēšanas metodes telpiskā saskaņošana	Novērtēšanas vienādošana (Matching Estimator)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2000s–2010s	1973
Autors	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)
Tips	Quasi-experimental causal inference	Nonparametric matching / causal inference
Pirmavots	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.
Citi nosaukumi	geographic matching estimator, spatial nearest-neighbor matching, location-based matching estimator, spatially-weighted matching	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Saistītās	6	6
Kopsavilkums	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.