Privacy-Robust Incrementality Measurement for Advertising Systems under Signal Loss

Problem
This work addresses the challenge of measuring incrementality in advertising systems while adhering to privacy constraints, which often lead to significant signal loss. The authors highlight that traditional randomized lift tests are compromised by various forms of signal degradation, including match-rate loss and randomized reporting noise. This paper is a preprint and thus has not undergone peer review, indicating that the findings should be interpreted with caution.

Method
The authors propose a robust causal decision framework that formulates the problem of privacy-constrained advertising measurement. They define an ambiguity set to account for privacy-induced degradation and project the observation-compatible fiber of clean experimental worlds onto the incrementality functional. The framework yields a decision frontier that categorizes reports into certified, rejected, or unresolved decisions based on the amount of information available. Key contributions include finite-sample certification, sample-complexity guarantees, and a minimax lower bound that illustrates the impact of signal loss on effective information. The method also explores a tradeoff between reporting granularity and the robustness of incrementality measurement.

Results
The framework was evaluated on two datasets: the Criteo Uplift dataset (2.0 million rows) and the Hillstrom email experiment (64,000 rows). The clean conversion lifts were reported as 0.00112 for Criteo and 0.00495 for Hillstrom, indicating positive incrementality in both cases. The authors found that population certification remained valid under mild degradation in the Criteo dataset and severe degradation in the Hillstrom dataset. However, all finite-sample stress tests across both datasets remained unresolved when accounting for simultaneous uncertainty and reporting noise, highlighting the limitations of the current approach in certain scenarios.

Limitations
The authors acknowledge that their framework may struggle with severe degradation scenarios, as evidenced by unresolved cases in the Hillstrom dataset. They also note that the decision frontier may not provide sufficient information for distinguishing between incrementality and non-incrementality in certain contexts. Additionally, the reliance on specific assumptions regarding the nature of signal loss may limit the generalizability of the findings.

Why it matters
This research contributes a significant advancement in the field of privacy-aware incrementality measurement, providing a structured approach to making causal claims despite the challenges posed by signal degradation. The decision-theoretic framework can inform future work in advertising analytics, particularly in developing more robust methodologies for incrementality measurement under privacy constraints. This is crucial as the industry increasingly prioritizes user privacy while seeking to maintain effective advertising strategies, as published in arXiv.