Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Policy Evaluation Entropy Balancing× | Käänteisen todennäköisyyden painotus (IPW / IPTW)× | |
|---|---|---|
| Tieteenala | Kausaalipäättely | Kausaalipäättely |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2012 | 2000 |
| Kehittäjä≠ | Jens Hainmueller | Robins, Hernán & Brumback |
| Tyyppi≠ | Preprocessing / reweighting estimator | Causal inference weighting estimator |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet≠ | Entropy Balancing, EB Weighting, Maximum-Entropy Reweighting, Hainmueller Balancing | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Liittyvät≠ | 4 | 5 |
| Tiivistelmä≠ | 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. |
| ScholarGateAineisto ↗ |
|
|