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| Bilanciamento Entropico di Dati Panel× | Ponderazione dell'Inverso della Probabilità per Dati Panel× | |
|---|---|---|
| Campo | Inferenza causale | Inferenza causale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2012 (cross-section); panel adaptation mid-2010s onward | 2000 |
| Ideatore≠ | Hainmueller (2012); extended to panel settings by subsequent applied econometric work | Robins, Hernan & Brumback |
| Tipo≠ | Covariate balancing / reweighting estimator | Reweighting / causal inference |
| Fonte seminale≠ | 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. DOI ↗ | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Alias | EB-panel, panel entropy balancing, entropy reweighting in panel data, panel-EB | panel IPW, longitudinal IPW, time-varying IPW, panel IPTW |
| Correlati | 5 | 5 |
| Sintesi≠ | 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. |
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