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Estimation of average treatment effects for massively unbalanced binary outcomes

Author

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  • Jinyong Hahn
  • Xueyuan Liu
  • Geert Ridder

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.

Suggested Citation

  • Jinyong Hahn & Xueyuan Liu & Geert Ridder, 2024. "Estimation of average treatment effects for massively unbalanced binary outcomes," Econometric Reviews, Taylor & Francis Journals, vol. 43(6), pages 319-344, July.
    • Handle: RePEc:taf:emetrv:v:43:y:2024:i:6:p:319-344
    DOI: 10.1080/07474938.2024.2330150
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