Linganisha mbinu

	Njia ya Dhibiti ya Syntetiki Inayobadilika	Tofauti-katika-Tofauti Inayobadilika
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2010	2021
Mwanzilishi	Abadie, Diamond & Hainmueller (2010); dynamic extensions by Abadie (2021) and others	Callaway & Sant'Anna; Sun & Abraham
Aina	Comparative case study / counterfactual estimation	Causal inference / quasi-experimental
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.	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
Majina mbadala	Dynamic SCM, Time-varying synthetic control, Multi-period synthetic control, DSC	Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD
Zinazohusiana	5	4
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.	Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time.