Linganisha mbinu

	Tofauti Sintetiki-ya-Tofauti	Utafiti wa Kijografia wa Kukatizwa kwa Regresi
Nyanja	Ekonometriki	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	2021	2010
Mwanzilishi	Arkhangelsky, Athey, Hirshberg, Imbens, and Wager	Melissa Dell and colleagues
Aina	Treatment-effect estimation	Spatial quasi-experiment
Chanzo asilia	Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021). Synthetic difference-in-differences. American Economic Review, 111(12), 4088-4118.	Dell, M. (2018). The persistent effects of Peru's mining mita. Econometrica, 78(6), 1863-1911.
Majina mbadala	Synthetic DID, SDID	Spatial RD, Geographic RDD
Zinazohusiana	3	3
Muhtasari	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.	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.