Linganisha mbinu

	Njia ya Dhibiti ya Syntetiki Inayobadilika	Njia ya Kidhibiti Sanisi cha Data ya Paneli
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2010	2010
Mwanzilishi	Abadie, Diamond & Hainmueller (2010); dynamic extensions by Abadie (2021) and others	Alberto Abadie, Alexis Diamond & Jens Hainmueller
Aina	Comparative case study / counterfactual estimation	Causal inference / panel data
Chanzo asilia	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.	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.
Majina mbadala	Dynamic SCM, Time-varying synthetic control, Multi-period synthetic control, DSC	SCM panel, panel synthetic control, synthetic control estimator, comparative case study
Zinazohusiana	5	5
Muhtasari	The Dynamic Synthetic Control Method extends the classic synthetic control framework to evaluate treatments that unfold over multiple periods or change in intensity over time. It constructs a weighted combination of untreated units that matches the treated unit in pre-treatment outcomes, then traces the full time path of treatment effects period by period after the intervention — capturing not just an average effect but how the effect evolves dynamically.	The panel data synthetic control method estimates the causal effect of an intervention on a single treated unit by constructing a data-driven weighted combination of untreated units — a synthetic control — that best reproduces the treated unit's pre-treatment outcome trajectory. The post-treatment gap between the treated unit and its synthetic counterpart is the estimated treatment effect.