방법 비교

	이질적 처리 효과 반사실적 영향 평가	반사실적 영향 평가 (CIE)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2010s	1970s–2000s
창시자	Cerulli (2010) for CIE framework; Athey & Wager (2019) for causal forest-based CATE within CIE	Heckman, Imbens, Rubin, and the program evaluation literature
유형	Quasi-experimental causal inference with subgroup heterogeneity	Causal inference / program evaluation
원전	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.
별칭	HTE-CIE, heterogeneous CIE, CATE-based counterfactual evaluation, subgroup counterfactual impact evaluation	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation
관련	4	5
요약	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.