Salīdzināt metodes

	Paneļdatu entropijas balansēšana	Panelu datu tendenču rādītāju saskaņošana
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2012 (cross-section); panel adaptation mid-2010s onward	1997-1998
Autors	Hainmueller (2012); extended to panel settings by subsequent applied econometric work	Heckman, Ichimura & Todd
Tips	Covariate balancing / reweighting estimator	Matching / causal inference
Pirmavots	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.	Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an Econometric Evaluation Estimator. Review of Economic Studies, 65(2), 261-294.
Citi nosaukumi	EB-panel, panel entropy balancing, entropy reweighting in panel data, panel-EB	PSM with panel data, longitudinal PSM, panel PSM, difference-in-differences PSM
Saistītās	5	6
Kopsavilkums	Panel data entropy balancing extends Hainmueller's (2012) entropy balancing method to longitudinal settings. It computes unit-level weights for control observations so that their covariate moments exactly match those of the treatment group across panel periods, then plugs these weights into a weighted panel regression to estimate causal treatment effects without requiring a correctly specified propensity score model.	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.