Multicalibration for Unbiased Model-Based Prevalence Estimation
Quick summary
arXiv:2604.21549v2 Announce Type: replace Abstract: Estimating the prevalence of a category in a population using imperfect measurement devices (diagnostic tests, classifiers, or large language models) is fundamental to science, public health, and online trust and safety. Standard approaches correct for known device error rates but assume these rates remain stable across populations. We show this assumption fails under covariate shift and that multicalibration, which enforces calibration conditional on the input features rather than just on average, is sufficient for unbiased prevalence estima
Key takeaways
- arXiv:2604.21549v2 Announce Type: replace Abstract: Estimating the prevalence of a category in a population using imperfect measurement devices (diagnostic tests, classifiers, or large language models) is fundamental to science, public health, and online trust and safety.
- Standard approaches correct for known device error rates but assume these rates remain stable across populations.
- We show this assumption fails under covariate shift and that multicalibration, which enforces calibration conditional on the input features rather than just on average, is sufficient for unbiased prevalence estima
Why it matters
“Multicalibration for Unbiased Model-Based Prevalence Estimation” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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