So sánh phương pháp

	Robust Propensity Score Matching	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	2016 (robust variance correction); 1983 (PSM foundations)	2000
Người khởi xướng	Abadie & Imbens (2016) for matching-on-estimated-propensity-score with corrected variance; Rosenbaum & Rubin (1983) for PSM foundations	Robins, Hernán & Brumback
Loại	Quasi-experimental matching estimator with robust inference	Causal inference weighting estimator
Công trình gốc	Abadie, A., & Imbens, G. W. (2016). Matching on the Estimated Propensity Score. Econometrica, 84(2), 781-807.	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	robust PSM, PSM with robust variance, bias-corrected PSM, matching with robust inference	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Liên quan	6	5
Tóm tắt	Robust Propensity Score Matching (robust PSM) is a quasi-experimental causal inference method that pairs treated and control units on their estimated probability of receiving treatment (the propensity score), then estimates the average treatment effect using variance estimators that account for the uncertainty introduced by estimating the propensity score itself. The correction, developed by Abadie and Imbens (2016), prevents misleading inference that standard bootstrap or analytic formulas produce when applied naively after matching.	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.