So sánh phương pháp

	Mô hình cấu trúc biên (MSM)	G-Computation (Công thức G tham số)
Lĩnh vực	Suy luận nhân quả	Suy luận nhân quả
Họ	Regression model	Regression model
Năm ra đời	2000	1986
Người khởi xướng	James M. Robins, Miguel A. Hernan, Babette Brumback	James M. Robins
Loại	Causal model / semiparametric weighting	Parametric causal effect estimation
Công trình gốc	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Robins, J. M. (1986). A new approach to causal inference in mortality studies with sustained exposure periods: application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512.
Tên gọi khác	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model	G-formula, Parametric G-formula, Standardization
Liên quan	5	2
Tóm tắt	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.	G-computation is a causal inference method for estimating the effect of an intervention or treatment on an outcome from observational data. Developed by James M. Robins in 1986, it provides a parametric approach to standardization that can handle time-varying exposures and confounders. The method estimates what the population outcome would be under different intervention scenarios by utilizing fitted outcome models.