arXiv:2606.03878v1 Announce Type: cross 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. This paper formulates privacy-constrained advertising measurement as a robust causal decision problem under the mentioned signal losses. Given a randomized experiment and an ambiguity set for privacy-induced degradation, the framework p
The increasing focus on privacy regulations and data protection necessitates new methods for advertising measurement that can operate effectively under signal loss, a growing challenge for platforms.
Maintaining effective advertising measurement while adhering to privacy standards directly impacts business models reliant on advertising revenue and the ability to demonstrate ROI in marketing spend.
This research introduces a robust causal decision framework to quantify advertising incrementality despite significant privacy-induced data degradation, offering a structured approach to a persistent problem.
- · Advertising platforms
- · Advertisers
- · Privacy-focused ad tech companies
- · Companies reliant on pre-privacy data granularity
- · Traditional ad measurement methodologies
More accurate advertising incrementality measurements become possible even with stringent privacy controls, improving campaign optimization.
Increased advertiser trust and spending on platforms that can demonstrate ROI under privacy constraints could lead to market share shifts.
The development of a standardized, privacy-preserving measurement framework could become an industry norm, influencing future regulatory approaches.
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Read at arXiv cs.LG