Vertaile menetelmiä

	Robusti synteettisen kontrollin menetelmä	Erojen erot (Diff-in-Diff)
Tieteenala	Kausaalipäättely	Ekonometria
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	2021	1994
Kehittäjä	Cattaneo, Feng & Titiunik (2021); building on Abadie, Diamond & Hainmueller (2010)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tyyppi	Quasi-experimental causal inference	Causal inference / panel regression
Alkuperäislähde	Cattaneo, M. D., Feng, Y., & Titiunik, R. (2021). Prediction Intervals for Synthetic Control Methods. Journal of the American Statistical Association, 116(536), 1865-1880.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Rinnakkaisnimet	Robust SCM, Inference-robust synthetic control, Synthetic control with valid inference, SCM with prediction intervals	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Liittyvät	5	5
Tiivistelmä	The robust synthetic control method extends the classic synthetic control estimator by providing statistically valid uncertainty quantification and inference. Developed by Cattaneo, Feng and Titiunik (2021), it addresses a core limitation of the original approach — the lack of formal prediction intervals — making causal conclusions more defensible when only a single treated unit is observed.	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.