Salīdzināt metodes

	Sintētiskās kontroles metodes heterogēni ietekmes novērtējums	Diferenču starpībām (Diff-in-Diff)
Nozare	Cēloņsakarību secināšana	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	2010-2021	1994
Autors	Abadie, Diamond & Hainmueller (SCM foundation); Ben-Michael, Feller & Rothstein (augmented/HTE extensions)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tips	Quasi-experimental causal inference	Causal inference / panel regression
Pirmavots	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
Citi nosaukumi	HTE-SCM, heterogeneous SCM, heterogeneous synthetic control, SCM with HTE	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Saistītās	6	5
Kopsavilkums	The Heterogeneous Treatment Effect Synthetic Control Method (HTE-SCM) extends the classical synthetic control framework by allowing the causal effect of an intervention to vary across time periods, subgroups, or outcome dimensions rather than collapsing it to a single average estimate. It combines the counterfactual donor-pool matching logic of Abadie et al. (2010) with modern heterogeneous-effects machinery to recover time-varying or subgroup-specific treatment paths.	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.