방법 비교

	동적 반사실적 영향 평가	반사실적 영향 평가 (CIE)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	1986–2009	1970s–2000s
창시자	Robins (1986); Lechner (2009) for sequential treatment settings	Heckman, Imbens, Rubin, and the program evaluation literature
유형	Causal inference / program evaluation	Causal inference / program evaluation
원전	Robins, J. M. (1986). A new approach to causal inference in mortality studies with a sustained exposure period — application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512.	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	dynamic CIE, dynamic treatment evaluation, time-varying counterfactual analysis, longitudinal counterfactual evaluation	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation
관련	6	5
요약	Dynamic Counterfactual Impact Evaluation (dynamic CIE) extends standard counterfactual program evaluation to settings where treatment is assigned sequentially across multiple periods. Rather than comparing a single treated versus untreated state, it estimates the causal effect of entire treatment trajectories or regimes, accounting for how intermediate outcomes and time-varying covariates feed back into subsequent treatment decisions.	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.