Estimation of average treatment effects for massively unbalanced binary outcomes

Jinyong Hahn, Xueyuan Liu and Geert Ridder

Econometric Reviews, 2024, vol. 43, issue 6, 319-344

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.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:taf:emetrv:v:43:y:2024:i:6:p:319-344

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DOI: 10.1080/07474938.2024.2330150

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