So sánh phương pháp

	Multi-period Coarsened Exact Matching (ghép khớp chính xác có rút gọn đa kỳ)	Cân bằng Entropy
Lĩnh vực	Suy luận nhân quả	Suy luận nhân quả
Họ	Regression model	Regression model
Năm ra đời	2012–2021	2012
Người khởi xướng	Iacus, King & Porro (CEM, 2012); extended to multi-period panel settings	Jens Hainmueller
Loại	Non-parametric matching / causal inference	Covariate-balancing reweighting
Công trình gốc	Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Political Analysis, 20(1), 1-24.	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	Multi-period CEM, Longitudinal CEM, Panel CEM, Multi-wave CEM	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Liên quan	6	6
Tóm tắt	Multi-period Coarsened Exact Matching (multi-period CEM) extends the CEM framework of Iacus, King, and Porro to longitudinal data with multiple pre- and post-treatment periods. It bins continuous covariates into coarsened categories, matches treated and control units that fall into the same cells across all relevant time periods, and then estimates a weighted average treatment effect that accounts for temporal structure.	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.