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	Spatial Counterfactual Impact Evaluation (SCIE)	Propensity score-matchning
Ämnesområde	Kausal inferens	Forskningsstatistik
Familj	Regression model	Process / pipeline
Ursprungsår	2010s	1983
Upphovsperson	Cerqua, Pellegrini, and regional-science scholars building on counterfactual econometrics	Paul Rosenbaum and Donald Rubin
Typ	Quasi-experimental / causal inference	Method
Ursprungskälla	Cerqua, A., & Pellegrini, G. (2014). Do subsidies to private capital boost firms' growth? A multiple regression discontinuity design approach. Journal of Public Economics, 109, 114-126.	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	SCIE, spatial CIE, place-based counterfactual evaluation, regional counterfactual analysis	PSM, propensity score weighting, covariate balance
Närliggande	5	3
Sammanfattning	Spatial Counterfactual Impact Evaluation (SCIE) is a family of quasi-experimental methods that estimate the causal effect of geographically targeted policies — such as EU Cohesion Funds, enterprise zones, or place-based subsidies — by constructing a spatial counterfactual: what outcomes the treated region would have experienced without the intervention, inferred from comparable untreated regions or from discontinuities at policy boundaries.	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.