Linganisha mbinu

	Ulinganishaji wa Alama ya Mwelekeo wa Data ya Paneli	Usawazishaji wa Entropia
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	1997-1998	2012
Mwanzilishi	Heckman, Ichimura & Todd	Jens Hainmueller
Aina	Matching / causal inference	Covariate-balancing reweighting
Chanzo asilia	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.
Majina mbadala	PSM with panel data, longitudinal PSM, panel PSM, difference-in-differences PSM	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Zinazohusiana	6	6
Muhtasari	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.