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Estimation of average treatment effects for massively unbalanced binary outcomes.
- Source :
-
Econometric Reviews . 2024, Vol. 43 Issue 6, p319-344. 26p. - Publication Year :
- 2024
-
Abstract
- The MLE of the ATE in the logit model for binary outcomes may have a significant second-order bias if the event has a low probability, which is the case we focus on in this article. We derive the second-order bias of the logit ATE estimator, and we propose a bias-corrected estimator of the ATE. We also propose a variation on the logit model with parameters that are elasticities. Finally, we propose a computational trick that avoids numerical instability in the case of estimation for rare events. [ABSTRACT FROM AUTHOR]
- Subjects :
- *TREATMENT effectiveness
*LOGISTIC regression analysis
*ELASTICITY
Subjects
Details
- Language :
- English
- ISSN :
- 07474938
- Volume :
- 43
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Econometric Reviews
- Publication Type :
- Academic Journal
- Accession number :
- 177800099
- Full Text :
- https://doi.org/10.1080/07474938.2024.2330150