Introduces a framework that converts sparse incrementality experiment lifts into daily attribution corrections under structural constraints, reducing calibration error and measured cannibalization rate by ~15pp in TikTok deployments.
LiDDA: Data Driven Attribution at LinkedIn
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abstract
Data Driven Attribution, which assigns conversion credits to marketing interactions based on causal patterns learned from data, is the foundation of modern marketing intelligence and vital to any marketing business and advertising platform. In this paper, we introduce a unified transformer-based attribution approach that can handle member-level data, aggregate-level data, and integration of external macro factors. We detail the large scale implementation of the approach at LinkedIn, showcasing significant impact. We also share learnings and insights which are broadly applicable to the marketing and ad tech fields.
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cs.IR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Attributed, But Not Incremental: Cannibalization-Corrected Attribution for Large-Scale Advertising
Introduces a framework that converts sparse incrementality experiment lifts into daily attribution corrections under structural constraints, reducing calibration error and measured cannibalization rate by ~15pp in TikTok deployments.