方法对比

	多期粗糙化精确匹配	熵平衡
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	2012–2021	2012
提出者	Iacus, King & Porro (CEM, 2012); extended to multi-period panel settings	Jens Hainmueller
类型	Non-parametric matching / causal inference	Covariate-balancing reweighting
开创性文献	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.
别名	Multi-period CEM, Longitudinal CEM, Panel CEM, Multi-wave CEM	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
相关	6	6
摘要	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.