방법 비교

	다기간 반사실적 영향 평가	Marginal Structural Model (MSM)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2000s–2010s	2000
창시자	Developed through EU policy evaluation practice (European Commission); formalized by Lechner, Caliendo, and related econometricians	James M. Robins, Miguel A. Hernan, Babette Brumback
유형	Causal inference / quasi-experimental evaluation	Causal model / semiparametric weighting
원전	Caliendo, M., & Kopeinig, S. (2008). Some Practical Guidance for the Implementation of Propensity Score Matching. Journal of Economic Surveys, 22(1), 31-72.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
별칭	multi-period CIE, longitudinal counterfactual evaluation, dynamic counterfactual impact evaluation, multi-wave CIE	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
관련	4	5
요약	Multi-period Counterfactual Impact Evaluation (CIE) estimates the causal effect of a policy or program by constructing what would have happened to treated units across multiple time periods had they not been treated. Unlike single-period evaluations, it tracks treatment effects as they evolve over time, capturing dynamic, delayed, or fading impacts that a two-period comparison would miss.	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.