Vertaile menetelmiä

	Alueellinen regressio-katkoskohtasuunnittelu (Spatial RDD)	Propensity Score Matching
Tieteenala	Kausaalipäättely	Tutkimuksen tilastomenetelmät
Menetelmäperhe	Regression model	Process / pipeline
Syntyvuosi	2010s	1983
Kehittäjä	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)	Paul Rosenbaum and Donald Rubin
Tyyppi	Quasi-experimental causal inference	Method
Alkuperäislähde	Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.	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.
Rinnakkaisnimet	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design	PSM, propensity score weighting, covariate balance
Liittyvät	4	3
Tiivistelmä	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.	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.