Bandingkan metode

	Metode Kontrol Sintetis Dinamis	Metode Kontrol Sintetis Data Panel
Bidang	Inferensi Kausal	Inferensi Kausal
Keluarga	Regression model	Regression model
Tahun asal	2010	2010
Pencetus	Abadie, Diamond & Hainmueller (2010); dynamic extensions by Abadie (2021) and others	Alberto Abadie, Alexis Diamond & Jens Hainmueller
Tipe	Comparative case study / counterfactual estimation	Causal inference / panel data
Sumber perintis	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.
Alias	Dynamic SCM, Time-varying synthetic control, Multi-period synthetic control, DSC	SCM panel, panel synthetic control, synthetic control estimator, comparative case study
Terkait	5	5
Ringkasan	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.