Attributed, But Not Incremental: Cannibalization-Corrected Attribution for Large-Scale Advertising
In large-scale paid acquisition and growth advertising systems, production attribution outputs are widely used for daily budget allocation and channel diagnosis. However, paid-attributed conversions such as daily new users (DNU) may systematically overstate true incremental growth when paid channels overlap with organic demand, brand-driven traffic, or other acquisition channels. This attribution-cannibalization mismatch can distort incremental ROI measurement and budget decisions at scale. We propose an experiment-calibrated attribution correction framework that uses incrementality experiments as causal anchors to convert sparse lift measurements into daily correction estimates. To make ...
| Authors | Donghui Li, Bowen Yuan, Zili Yang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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