Linganisha mbinu

	Spatial Propensity Score Matching	Muundo wa Usambazaji wa Regression wa Kijiografia (Spatial RDD)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2000s	2010s
Mwanzilishi	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)
Aina	Quasi-experimental matching estimator	Quasi-experimental causal inference
Chanzo asilia	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.
Majina mbadala	Spatial PSM, Geospatial PSM, Spatially-adjusted propensity score matching, Geographic propensity score matching	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Zinazohusiana	6	4
Muhtasari	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.