方法对比

	面板数据倾向得分匹配	熵平衡
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	1997-1998	2012
提出者	Heckman, Ichimura & Todd	Jens Hainmueller
类型	Matching / causal inference	Covariate-balancing reweighting
开创性文献	Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an Econometric Evaluation Estimator. Review of Economic Studies, 65(2), 261-294.	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	PSM with panel data, longitudinal PSM, panel PSM, difference-in-differences PSM	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
相关	6	6
摘要	Panel data propensity score matching combines the bias-reduction of PSM with the longitudinal structure of panel data, enabling causal estimation of treatment effects by matching treated and control units on observable pre-treatment characteristics and then differencing within matched pairs over time. Developed in the framework of Heckman, Ichimura, and Todd (1998), it is especially valuable when randomisation is infeasible and both selection on observables and time-varying confounding must be addressed simultaneously.	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.