Formulates privacy-constrained advertising measurement as a robust causal decision problem under signal loss and derives a sharp decision frontier separating certifiable from unresolved incrementality claims.
Jon Vaver and Jim Koehler
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
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Privacy-Robust Incrementality Measurement for Advertising Systems under Signal Loss
Formulates privacy-constrained advertising measurement as a robust causal decision problem under signal loss and derives a sharp decision frontier separating certifiable from unresolved incrementality claims.
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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.