방법 비교

	이질적 처리 효과 반사실적 영향 평가	Marginal Structural Model (MSM)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2010s	2000
창시자	Cerulli (2010) for CIE framework; Athey & Wager (2019) for causal forest-based CATE within CIE	James M. Robins, Miguel A. Hernan, Babette Brumback
유형	Quasi-experimental causal inference with subgroup heterogeneity	Causal model / semiparametric weighting
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.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
별칭	HTE-CIE, heterogeneous CIE, CATE-based counterfactual evaluation, subgroup counterfactual impact evaluation	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
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.	A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail.