방법 비교

	다기간 성향 점수 가중치	Marginal Structural Model (MSM)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2000	2000
창시자	Robins, Hernán, and Brumback (building on Robins' g-computation framework)	James M. Robins, Miguel A. Hernan, Babette Brumback
유형	Quasi-experimental causal inference	Causal model / semiparametric weighting
원전	Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
별칭	longitudinal propensity score weighting, multi-wave PSW, time-varying propensity score weighting, sequential propensity score weighting	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
관련	5	5
요약	Multi-period propensity score weighting extends the standard propensity score weighting framework to settings with repeated measurements and time-varying treatments. It constructs stabilised inverse probability weights (IPW) at each time point so that the weighted sample resembles a sequence of randomised experiments, allowing unbiased estimation of causal effects under longitudinal confounding.	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.