เปรียบเทียบวิธี

	Spatial Counterfactual Impact Evaluation (SCIE)	ระเบียบวิธีสังเคราะห์การควบคุม (Synthetic Control Method - SCM)
สาขาวิชา	การอนุมานเชิงสาเหตุ	การอนุมานเชิงสาเหตุ
ตระกูล	Regression model	Regression model
ปีกำเนิด	2010s	2003–2010
ผู้ริเริ่ม	Cerqua, Pellegrini, and regional-science scholars building on counterfactual econometrics	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
ประเภท	Quasi-experimental / causal inference	Quasi-experimental causal inference
แหล่งต้นตำรับ	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.	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.
ชื่อเรียกอื่น	SCIE, spatial CIE, place-based counterfactual evaluation, regional counterfactual analysis	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
ที่เกี่ยวข้อง	5	4
สรุป	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.	The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.