विधियों की तुलना करें

	नीति मूल्यांकन के लिए सिंथेटिक कंट्रोल विधि	अंतर-में-अंतर (डिफ-इन-डिफ)
क्षेत्र	कारणात्मक अनुमान	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	2003-2010	1994
प्रवर्तक	Alberto Abadie & Javier Gardeazabal; extended by Abadie, Diamond & Hainmueller	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
प्रकार	Causal inference / comparative case study	Causal inference / panel regression
मौलिक स्रोत	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.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
उपनाम	Synthetic Control Method, SCM, Synthetic Control, Abadie-Diamond-Hainmueller method	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
संबंधित	5	5
सारांश	The Synthetic Control Method (SCM) is a causal inference technique for evaluating the effect of a policy or intervention on a single treated unit — such as a region, country, or firm — by constructing a weighted combination of untreated comparison units that closely mirrors the treated unit before the intervention. Introduced by Abadie and Gardeazabal (2003) and formalized by Abadie, Diamond, and Hainmueller (2010), it provides a data-driven, transparent counterfactual for comparative case studies.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.