Summary of Learning Metrics That Maximise Power For Accelerated A/b-tests, by Olivier Jeunen and Aleksei Ustimenko
Learning Metrics that Maximise Power for Accelerated A/B-Tests
by Olivier Jeunen, Aleksei Ustimenko
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR); Applications (stat.AP); Machine Learning (stat.ML)
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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 In this paper, researchers investigate ways to improve online controlled experiments in technology companies. They propose using North Star metrics, which measure long-term outcomes like revenue or user retention, to determine the best system variant. However, they note that these metrics can be delayed and insensitive, leading to high costs and frequent type-II errors (false negatives). To address this issue, the authors develop methods to optimize experimentation, potentially reducing the cost and increasing confidence in decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making better decisions in technology companies. They want to find the best way to make changes to a system or product. Right now, it’s hard because they need to test lots of different versions for a long time and even then, they might not get the right answer. The researchers are trying to figure out how to make this process better so that companies can make more confident decisions. |
