Comparar métodos

	Balance de Entropía para Datos de Panel	Ponderación por Probabilidad Inversa de Datos de Panel
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2012 (cross-section); panel adaptation mid-2010s onward	2000
Autor original	Hainmueller (2012); extended to panel settings by subsequent applied econometric work	Robins, Hernan & Brumback
Tipo	Covariate balancing / reweighting estimator	Reweighting / causal inference
Fuente seminal	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.
Alias	EB-panel, panel entropy balancing, entropy reweighting in panel data, panel-EB	panel IPW, longitudinal IPW, time-varying IPW, panel IPTW
Relacionados	5	5
Resumen	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.