So sánh phương pháp

	Ước lượng Khớp Động	Mô hình cấu trúc biên (MSM)
Lĩnh vực	Suy luận nhân quả	Suy luận nhân quả
Họ	Regression model	Regression model
Năm ra đời	2010	2000
Người khởi xướng	Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998)	James M. Robins, Miguel A. Hernan, Babette Brumback
Loại	Nonparametric causal inference / matching	Causal model / semiparametric weighting
Công trình gốc	Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137.	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	dynamic treatment matching, sequential matching estimator, dynamic selection-on-observables, DME	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Liên quan	6	5
Tóm tắt	The Dynamic Matching Estimator extends standard matching methods to settings where treatment is assigned sequentially over multiple periods. Instead of a single treatment decision, units receive or forgo treatment at each time point, and the estimator identifies causal effects of entire treatment histories by matching on time-varying covariates and past treatment paths, under sequential conditional independence assumptions.	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.