Salīdzināt metodes

	Paneļdatu entropijas balansēšana	Entropijas balansēšana
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2012 (cross-section); panel adaptation mid-2010s onward	2012
Autors	Hainmueller (2012); extended to panel settings by subsequent applied econometric work	Jens Hainmueller
Tips	Covariate balancing / reweighting estimator	Covariate-balancing reweighting
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.	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.
Citi nosaukumi	EB-panel, panel entropy balancing, entropy reweighting in panel data, panel-EB	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
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.	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.