So sánh phương pháp

	Ước lượng vững kép trong nghiên cứu giáo dục	Mô hình cấu trúc biên (MSM)
Lĩnh vực	Suy luận nhân quả	Suy luận nhân quả
Họ	Regression model	Regression model
Năm ra đời	1994-2005	2000
Người khởi xướng	Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)	James M. Robins, Miguel A. Hernan, Babette Brumback
Loại	Causal inference / semiparametric estimator	Causal model / semiparametric weighting
Công trình gốc	Bang, H., & Robins, J. M. (2005). Doubly Robust Estimation in Missing Data and Causal Inference Models. Biometrics, 61(4), 962-973.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Tên gọi khác	DR estimator in education, AIPW in education, augmented IPW in education research, doubly robust causal estimation for educational outcomes	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Liên quan	6	5
Tóm tắt	Doubly robust estimation (DR) is a semiparametric causal inference approach that combines an outcome regression model with a propensity score model. In education research, it is used to estimate the causal effect of educational programs, interventions, or policies on student outcomes when treatment assignment is non-random but observed covariates can account for selection bias. The estimator is consistent if either — not necessarily both — of the two component models is correctly specified.	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.