Compară metode

	Echilibrarea Entropică a Datelor de Tip Panel	Ponderarea prin probabilitatea inversă în date panel
Domeniu	Inferență cauzală	Inferență cauzală
Familie	Regression model	Regression model
Anul apariției	2012 (cross-section); panel adaptation mid-2010s onward	2000
Autorul original	Hainmueller (2012); extended to panel settings by subsequent applied econometric work	Robins, Hernan & Brumback
Tip	Covariate balancing / reweighting estimator	Reweighting / causal inference
Sursa seminală	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.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Denumiri alternative	EB-panel, panel entropy balancing, entropy reweighting in panel data, panel-EB	panel IPW, longitudinal IPW, time-varying IPW, panel IPTW
Înrudite	5	5
Rezumat	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 Inverse Probability Weighting (panel IPW) estimates the causal effect of a time-varying treatment by reweighting observed units to create a pseudo-population in which treatment is independent of measured confounders at each time point. It extends the cross-sectional IPW framework to longitudinal settings where treatment status and confounders both evolve across multiple periods.