Confronta i metodi

	Spatial Matching Estimator	Abbinamento del punteggio di propensione
Campo	Inferenza causale	Statistica per la ricerca
Famiglia	Regression model	Process / pipeline
Anno di origine	2000s–2010s	1983
Ideatore	Extension of Abadie & Imbens (2006) matching estimator to spatial settings; geographic applications developed in urban/environmental econometrics literature	Paul Rosenbaum and Donald Rubin
Tipo	Quasi-experimental causal inference	Method
Fonte seminale	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Alias	geographic matching estimator, spatial nearest-neighbor matching, location-based matching estimator, spatially-weighted matching	PSM, propensity score weighting, covariate balance
Correlati	6	3
Sintesi	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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.