So sánh phương pháp

	Bộ ước lượng Ghép Nối Hiệu ứng Điều trị Không đồng nhất	Ghép cặp điểm xu hướng
Lĩnh vực	Suy luận nhân quả	Thống kê nghiên cứu
Họ	Regression model	Process / pipeline
Năm ra đời	1997-2006	1983
Người khởi xướng	Heckman, Ichimura & Todd; Abadie & Imbens	Paul Rosenbaum and Donald Rubin
Loại	Causal inference / nonparametric matching	Method
Công trình gốc	Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme. Review of Economic Studies, 64(4), 605-654.	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	HTE matching, subgroup matching estimator, conditional matching estimator, CATE matching	PSM, propensity score weighting, covariate balance
Liên quan	6	3
Tóm tắt	The Heterogeneous Treatment Effect (HTE) Matching Estimator extends standard matching to recover how treatment impacts differ across subgroups or covariate values. Rather than reporting a single average treatment effect, it pairs treated and control units on observed characteristics and then estimates the conditional average treatment effect (CATE) as a function of those characteristics — revealing who benefits most, least, or not at all.	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.