Variance Reduction for Heavy-Tailed Monetization Metrics in Ranking Experiments via Post-Stratification
arXiv:2606.04110v1 Announce Type: new Abstract: Online evaluation of ranking and retrieval systems often relies on downstream monetization metrics such as app revenue or creator earnings. These metrics are typically heavy-tailed, with a small fraction of users dominating both mean and variance, leading to low statistical power and unreliable conclusions in A/B experiments -- especially under limited traffic. We present a practical framework for variance reduction in online experiments by combining post-stratification with CUPED. Our approach leverages pre-experiment covariates to improve the s
The increasing reliance on online experiments for optimizing monetization metrics in AI-driven ranking and retrieval systems necessitates more robust statistical methods. The prevalence of heavy-tailed distributions in these metrics creates a current challenge for accurate evaluation.
Improving the accuracy and statistical power of A/B tests for monetization metrics directly impacts revenue generation and product optimization for companies relying on AI for ranking and retrieval. This innovation enables quicker and more reliable product iterations.
The proposed framework combining post-stratification and CUPED offers a practical way to reduce variance in online experiments, leading to more reliable conclusions even with limited traffic. This changes the precision and efficiency of experimental design for online platforms.
- · Ad-tech companies
- · E-commerce platforms
- · Social media companies
- · AI/ML researchers and practitioners
- · Companies relying on less sophisticated A/B testing methodologies
Increased efficiency and effectiveness of online experiments for monetization metrics.
Faster iteration cycles and more accurate product improvements for online platforms.
Potentially better user experience and higher revenue generation for platforms that adopt advanced statistical methods in their A/B testing.
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Read at arXiv cs.LG