Sammenlign metoder

	Rumlig Propensity Score-vægtning	Spatial Regression Discontinuity Design (Spatial RDD)
Fagområde	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model
Oprindelsesår	2000s–2010s	2010s
Ophavsperson	Extended from Hirano, Imbens & Ridder (2003) IPTW with spatial adaptations by Keele, Titiunik and others in geographically structured causal designs	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)
Type	Quasi-experimental / causal inference	Quasi-experimental causal inference
Oprindelig kilde	Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155.	Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.
Aliasser	spatial PSW, geographically weighted propensity score weighting, spatial IPTW, spatially adjusted inverse probability weighting	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Relaterede	6	4
Resumé	Spatial propensity score weighting extends inverse probability of treatment weighting (IPTW) to settings where units are geographically located and treatment assignment may depend on spatial factors such as location, neighborhood characteristics, or spatial clustering. By incorporating spatial covariates into the propensity score model and adjusting standard errors for spatial autocorrelation, it produces more credible causal estimates from observational geographic data.	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.