Porovnať metódy
Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Vyváženie pomocou entropie pri hodnotení politík× | Vážená inverzná pravdepodobnosť liečby (IPW / IPTW)× | |
|---|---|---|
| Odbor | Kauzálna inferencia | Kauzálna inferencia |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 2012 | 2000 |
| Tvorca≠ | Jens Hainmueller | Robins, Hernán & Brumback |
| Typ≠ | Preprocessing / reweighting estimator | Causal inference weighting estimator |
| Pôvodný zdroj≠ | 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 ↗ |
| Ďalšie názvy≠ | Entropy Balancing, EB Weighting, Maximum-Entropy Reweighting, Hainmueller Balancing | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Príbuzné≠ | 4 | 5 |
| Zhrnutie≠ | 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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