Salīdzināt metodes

	Sintētiskās kontroles metodes heterogēni ietekmes novērtējums	Novērtēšanas vienādošana (Matching Estimator)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2010-2021	1973
Autors	Abadie, Diamond & Hainmueller (SCM foundation); Ben-Michael, Feller & Rothstein (augmented/HTE extensions)	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Tips	Quasi-experimental causal inference	Nonparametric matching / causal inference
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.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Citi nosaukumi	HTE-SCM, heterogeneous SCM, heterogeneous synthetic control, SCM with HTE	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Saistītās	6	6
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.	The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome.