Summary of State: a Robust Ate Estimator Of Heavy-tailed Metrics For Variance Reduction in Online Controlled Experiments, by Hao Zhou et al.
STATE: A Robust ATE Estimator of Heavy-Tailed Metrics for Variance Reduction in Online Controlled Experiments
by Hao Zhou, Kun Sun, Shaoming Li, Yangfeng Fan, Guibin Jiang, Jiaqi Zheng, Tao Li
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 propose a novel approach to online controlled experiments, which are crucial for data-driven decision-making in various industries. The authors focus on variance reduction techniques to improve the sensitivity of experiments while using fewer samples and shorter experimental periods. They argue that traditional methods rely on Gaussian distributions and fail to account for heavy-tailed real-world business metrics. Additionally, outliers can significantly limit the effectiveness of these methods by diminishing correlations between pre-experiment covariates and outcome metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Online controlled experiments are super important because they help companies make informed decisions based on data. The problem is that most current approaches don’t work well with real-world data that has weird patterns or “heavy-tailed” distributions. This makes it hard to get accurate results. The authors suggest a new way to improve these experiments, making them more efficient and reliable. |
