Summary of Variance Reduction in Ratio Metrics For Efficient Online Experiments, by Shubham Baweja et al.
Variance Reduction in Ratio Metrics for Efficient Online Experiments
by Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko, Olivier Jeunen
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents a solution to the efficiency problem in online controlled experiments, specifically A/B-tests. The authors aim to reduce the variance of online metrics, which currently hinders the accuracy and speed of these experiments. By developing a novel method for ratio metrics, such as click-through rate or user retention, the authors hope to improve the statistical significance and efficiency of A/B-tests. This could have significant implications for modern tech companies that rely heavily on continuous system improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is trying to make online experiments more efficient. Online tests are important because they help companies improve their systems, but they can be expensive and sometimes don’t give clear answers. The problem is that the metrics used in these tests (like how many people click on something) can be very variable, which makes it hard to get accurate results. The authors of this paper are trying to come up with a way to reduce this variability, so companies can make better decisions faster. |
