Estimate Level Adjustment For Inference With Proxies Under Random Distribution Shifts
In many scientific domains, including experimentation, researchers rely on measurements of proxy outcomes to achieve faster and more frequent reads, especially when the primary outcome of interest is challenging to measure directly. While proxies offer a more readily accessible observation for inference, the ultimate goal is to draw statistical inferences about the primary outcome parameter and proxy data are typically imperfect in some ways. To correct for these imperfections, current statistical inference methods often depend on strict identifying assumptions (such as surrogacy, covariate/label shift, or missingness assumptions). These assumptions can be difficult to validate and may be...
| Authors | Steven Wilkins-Reeves, Alexandra N. M. Darmon, Deeksha Sinha |
|---|---|
| Primary category | stat.ME |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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