So sánh phương pháp

	Trọng số xác suất nghịch đảo mạnh mẽ (Robust IPW)	Ghép cặp điểm xu hướng
Lĩnh vực	Suy luận nhân quả	Thống kê nghiên cứu
Họ	Regression model	Process / pipeline
Năm ra đời	2000-2004	1983
Người khởi xướng	Lunceford & Davidian (2004); Robins, Hernán & Brumback (2000)	Paul Rosenbaum and Donald Rubin
Loại	Causal weighting estimator	Method
Công trình gốc	Lunceford, J. K., & Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine, 23(19), 2937-2960.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Tên gọi khác	Robust IPW, Stabilized IPW, Trimmed IPW, Variance-robust IPW	PSM, propensity score weighting, covariate balance
Liên quan	5	3
Tóm tắt	Robust Inverse Probability Weighting is a causal inference estimator that reweights observed units by stabilized or trimmed propensity score weights, then applies sandwich or bootstrap variance estimation to guard against model misspecification, extreme weights, and inflated standard errors. It extends standard IPW to improve finite-sample performance and inferential reliability in observational studies.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.