Võrdle meetodeid

	Heterogeense mõju hinnangu vastumõju hindamine	Kontrafaktuaalne mõju hindamine (CIE)
Valdkond	Põhjuslik järeldamine	Põhjuslik järeldamine
Perekond	Regression model	Regression model
Tekkeaasta	2010s	1970s–2000s
Looja	Cerulli (2010) for CIE framework; Athey & Wager (2019) for causal forest-based CATE within CIE	Heckman, Imbens, Rubin, and the program evaluation literature
Tüüp	Quasi-experimental causal inference with subgroup heterogeneity	Causal inference / program evaluation
Algallikas	Cerulli, G. (2010). Modelling and measuring the effect of public subsidies on business R&D: A critical review of the econometric literature. Economic Record, 86(274), 421-449.	Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. Handbook of Econometrics, 6B, 4779-4874.
Rööpnimetused	HTE-CIE, heterogeneous CIE, CATE-based counterfactual evaluation, subgroup counterfactual impact evaluation	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation
Seotud	4	5
Kokkuvõte	Heterogeneous Treatment Effect Counterfactual Impact Evaluation (HTE-CIE) extends standard counterfactual impact evaluation by estimating how the causal effect of a policy or intervention varies across subgroups defined by pre-treatment characteristics. Rather than reporting a single average treatment effect, it maps the Conditional Average Treatment Effect (CATE) across the covariate space, revealing who benefits most or least from an intervention.	Counterfactual Impact Evaluation is a family of causal methods that estimates the effect of an intervention by comparing what actually happened to participants with what would have happened had the intervention not taken place. Formalised in the Rubin Causal Model and extended by Heckman, Imbens and others, CIE underlies most modern program and policy evaluation practice.