Investigate missingness and add targeted labels: Which approach fits?
Investigate missingness and obtain targeted labels while reporting remaining uncertainty.
The question
A public-benefit agency is evaluating an eligibility-support model. Many records lack outcome labels, and missingness is concentrated among applicants with language-assistance needs. Before selecting between feasible validation methods, the agency requires evidence covering that underrepresented group and uncertainty from missing labels. Which approach fits?
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- Investigate missingness and add targeted labels. ✓Investigating the missingness pattern and obtaining targeted labels improves evidence for the affected group while leaving residual uncertainty visible.
- Restrict validation to complete-label records and compare subgroup estimates.Complete-case validation excludes the group with concentrated missing labels, so subgroup estimates remain incomplete and potentially biased.
- Report aggregate performance from available labels and describe the missingness limitation.Reporting the limitation is useful, but aggregate available-case results do not provide evidence for the affected underrepresented group.
- Impute missing outcomes using the majority observed outcome for validation.Majority imputation can mask concentrated missingness and create unjustified confidence about applicants needing language assistance.
The trap
Ask who is missing from labeled data and whether the missingness pattern could change performance conclusions. How to remember it
Investigate missingness and obtain targeted labels while reporting remaining uncertainty.
How many of these would you get right?
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