UA Ledger · Measurement Lab · draft for review

Bound a conversion share when some outcomes are unknown

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Start with one eligible population split into observed successes, observed failures and unknown outcomes. The lower bound assigns every unknown to failure; the upper bound assigns every unknown to success. Keep the unknown count separate from a genuine zero-valued observation. This distinction matters before choosing an imputation model or comparing groups with different observation coverage.

Calculated outputs

Assumption sensitivity

Only unknown varies; all other inputs are held constant. This is not a probability range.

Equations

• Lower success bound %: 100*success/total
• Upper success bound %: 100*(success+unknown)/total
• Known-only success %: 100*success/(total-unknown)

Additional constraints: (success+unknown<=total) && (unknown<total). The field minimums also apply.

Limits

The bounds require a correct eligible population and mutually exclusive states. They do not identify why signal is missing or model privacy-related suppression, attribution eligibility or selection bias.

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