So sánh phương pháp

	Bộ ước lượng Ghép Nối Hiệu ứng Điều trị Không đồng nhất	Cân bằng Entropy
Lĩnh vực	Suy luận nhân quả	Suy luận nhân quả
Họ	Regression model	Regression model
Năm ra đời	1997-2006	2012
Người khởi xướng	Heckman, Ichimura & Todd; Abadie & Imbens	Jens Hainmueller
Loại	Causal inference / nonparametric matching	Covariate-balancing reweighting
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.	Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.
Tên gọi khác	HTE matching, subgroup matching estimator, conditional matching estimator, CATE matching	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Liên quan	6	6
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.	Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step.