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Privacy-Robust Incrementality Measurement for Advertising Systems under Signal Loss

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arXiv:2606. 03878v1 Announce Type: new Abstract: Advertising platforms use randomized lift tests to measure incrementality, but privacy-preserving reporting systems degrade the observed signal through match-rate loss, linkability loss, attribution-window loss, aggregation-threshold suppression, randomized reporting noise, and segment-heterogeneous signal loss.

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Authors: Prashant Shekhar, Caroline Howard
Released: 2026-06-03 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Privacy-Robust Incrementality Measurement for Advertising Systems under Signal Loss overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, under, rate, project, trained, test. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.03878

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