Compară metode

	Potrivirea spațială a scorului de propensitate	Designul de Regresie cu Discontinuitate Spațială (RDD Spațial)
Domeniu	Inferență cauzală	Inferență cauzală
Familie	Regression model	Regression model
Anul apariției	2000s	2010s
Autorul original	Extension of Rosenbaum & Rubin (1983) PSM to spatial settings; spatial adaptation developed in applied econometrics and epidemiology literature from the 2000s onward	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)
Tip	Quasi-experimental matching estimator	Quasi-experimental causal inference
Sursa seminală	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.	Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.
Denumiri alternative	Spatial PSM, Geospatial PSM, Spatially-adjusted propensity score matching, Geographic propensity score matching	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Înrudite	6	4
Rezumat	Spatial Propensity Score Matching (Spatial PSM) extends the classic propensity score matching framework to settings where units are embedded in geographic space and treatment assignment or outcomes may be spatially correlated. By incorporating spatial covariates and adjacency structure into the propensity model and matching procedure, it produces causal estimates that account for geographic confounding and spillover effects.	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.