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| Entrópiával súlyozott politikai értékelés× | Az inverz valószínűségi kezelési súlyozás (IPW / IPTW)× | |
|---|---|---|
| Tudományterület | Oksági következtetés | Oksági következtetés |
| Módszercsalád | Regression model | Regression model |
| Keletkezés éve≠ | 2012 | 2000 |
| Megalkotó≠ | Jens Hainmueller | Robins, Hernán & Brumback |
| Típus≠ | Preprocessing / reweighting estimator | Causal inference weighting estimator |
| Alapmű≠ | 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., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Alternatív nevek≠ | Entropy Balancing, EB Weighting, Maximum-Entropy Reweighting, Hainmueller Balancing | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Kapcsolódó≠ | 4 | 5 |
| Összefoglaló≠ | Entropy balancing is a maximum-entropy reweighting method that assigns weights to control-group units so that their weighted covariate moments exactly match those of the treated group. Introduced by Hainmueller (2012), it provides exact balance on specified moments without iterative propensity-score trimming, making it a powerful preprocessing tool for causal policy evaluation in observational studies. | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. |
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