手法を比較

	地理回帰不連続デザイン	合成差分の差 (Synthetic Difference-in-Differences, SDID)
分野	計量経済学	計量経済学
系統	Regression model	Regression model
提唱年	2010	2021
提唱者	Melissa Dell and colleagues	Arkhangelsky, Athey, Hirshberg, Imbens, and Wager
種類	Spatial quasi-experiment	Treatment-effect estimation
原典	Dell, M. (2018). The persistent effects of Peru's mining mita. Econometrica, 78(6), 1863-1911.	Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021). Synthetic difference-in-differences. American Economic Review, 111(12), 4088-4118.
別名	Spatial RD, Geographic RDD	Synthetic DID, SDID
関連	3	3
概要	Geographic Regression Discontinuity (GRD) is a quasi-experimental design that exploits sharp geographic boundaries—borders, policy boundaries, or natural features—to estimate causal effects. Introduced by Dell (2010) and others, it compares outcomes on either side of a boundary where treatment changes abruptly, leveraging the idea that units on opposite sides of a border are otherwise similar. This approach yields credible causal estimates for spatially localized policies, institutional changes, and natural phenomena.	Synthetic Difference-in-Differences (SDID) combines synthetic control and difference-in-differences approaches to estimate treatment effects when a policy or intervention affects one unit (country, firm) at a point in time. Introduced by Arkhangelsky et al. (2021), it improves upon both methods alone by using weighted combinations of controls to match treated units' pre-treatment trends and levels. This yields more precise and robust estimates than classical DiD or synthetic control.