So sánh phương pháp

	Trọng số nghịch đảo xác suất hiệu ứng điều trị không đồng nhất (HTE-IPW)	Trọng số Xác suất Nghịch đảo của Điều trị (IPW / IPTW)
Lĩnh vực	Suy luận nhân quả	Suy luận nhân quả
Họ	Regression model	Regression model
Năm ra đời	2003–2015	2000
Người khởi xướng	Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli	Robins, Hernán & Brumback
Loại	Causal inference / weighted regression	Causal inference weighting estimator
Công trình gốc	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Tên gọi khác	HTE-IPW, CATE-IPW, heterogeneous IPW, conditional effect IPW	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Liên quan	5	5
Tóm tắt	HTE-IPW extends standard inverse probability weighting to recover how causal effects vary across subgroups or covariate values. By reweighting each observation by the inverse of its estimated treatment probability, the method creates a pseudo-population in which treatment is independent of background characteristics, and then estimates conditional average treatment effects (CATEs) as a function of those characteristics.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.