So sánh phương pháp

	Cân bằng Entropy	Ước lượng khớp cặp
Lĩnh vực	Suy luận nhân quả	Suy luận nhân quả
Họ	Regression model	Regression model
Năm ra đời	2012	1973
Người khởi xướng	Jens Hainmueller	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Loại	Covariate-balancing reweighting	Nonparametric matching / causal inference
Công trình gốc	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.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Tên gọi khác	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Liên quan	6	6
Tóm tắt	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.	The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome.