arXiv Open Access 2025

Reasonable uncertainty: Confidence intervals in empirical Bayes discrimination detection

Jiaying Gu Nikolaos Ignatiadis Azeem M. Shaikh

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Abstrak

We revisit empirical Bayes discrimination detection, focusing on uncertainty arising from both partial identification and sampling variability. While prior work has mostly focused on partial identification, we find that some empirical findings are not robust to sampling uncertainty. To better connect statistical evidence to the magnitude of real-world discriminatory behavior, we propose a counterfactual odds-ratio estimand with a attractive properties and interpretation. Our analysis reveals the importance of careful attention to uncertainty quantification and downstream goals in empirical Bayes analyses.

Topik & Kata Kunci

econ.EM stat.ME

Penulis (3)

Jiaying Gu

Nikolaos Ignatiadis

Azeem M. Shaikh

Format Sitasi

APA MLA BibTeX

Gu, J., Ignatiadis, N., Shaikh, A.M. (2025). Reasonable uncertainty: Confidence intervals in empirical Bayes discrimination detection. https://arxiv.org/abs/2508.13110

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓