Summary of A/b Testing Under Interference with Partial Network Information, by Shiv Shankar and Ritwik Sinha and Yash Chandak and Saayan Mitra and Madalina Fiterau
A/B testing under Interference with Partial Network Information
by Shiv Shankar, Ritwik Sinha, Yash Chandak, Saayan Mitra, Madalina Fiterau
First submitted to arxiv on: 16 Apr 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 This paper presents UNITE, a novel estimator that relaxes the assumption of knowing the exact underlying social network in A/B testing scenarios. Unlike prior works that require knowledge of the entire network, UNITE can identify the global average treatment effect (GATE) by only relying on information about a subject’s neighbors. The proposed approach is shown to outperform standard estimators through theoretical analysis and extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us do better A/B tests when we’re studying social media or trying to stop the spread of a disease. In these cases, it’s hard to get the whole network map, but we can still make good estimates if we know who someone is connected to. The new UNITE method makes this possible and works better than old ways. |
