A/B testing under Interference with Partial Network Information
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914757019172864 |
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| author | Shankar, Shiv Sinha, Ritwik Chandak, Yash Mitra, Saayan Fiterau, Madalina |
| author_facet | Shankar, Shiv Sinha, Ritwik Chandak, Yash Mitra, Saayan Fiterau, Madalina |
| contents | A/B tests are often required to be conducted on subjects that might have social connections. For e.g., experiments on social media, or medical and social interventions to control the spread of an epidemic. In such settings, the SUTVA assumption for randomized-controlled trials is violated due to network interference, or spill-over effects, as treatments to group A can potentially also affect the control group B. When the underlying social network is known exactly, prior works have demonstrated how to conduct A/B tests adequately to estimate the global average treatment effect (GATE). However, in practice, it is often impossible to obtain knowledge about the exact underlying network. In this paper, we present UNITE: a novel estimator that relax this assumption and can identify GATE while only relying on knowledge of the superset of neighbors for any subject in the graph. Through theoretical analysis and extensive experiments, we show that the proposed approach performs better in comparison to standard estimators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10547 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A/B testing under Interference with Partial Network Information Shankar, Shiv Sinha, Ritwik Chandak, Yash Mitra, Saayan Fiterau, Madalina Machine Learning A/B tests are often required to be conducted on subjects that might have social connections. For e.g., experiments on social media, or medical and social interventions to control the spread of an epidemic. In such settings, the SUTVA assumption for randomized-controlled trials is violated due to network interference, or spill-over effects, as treatments to group A can potentially also affect the control group B. When the underlying social network is known exactly, prior works have demonstrated how to conduct A/B tests adequately to estimate the global average treatment effect (GATE). However, in practice, it is often impossible to obtain knowledge about the exact underlying network. In this paper, we present UNITE: a novel estimator that relax this assumption and can identify GATE while only relying on knowledge of the superset of neighbors for any subject in the graph. Through theoretical analysis and extensive experiments, we show that the proposed approach performs better in comparison to standard estimators. |
| title | A/B testing under Interference with Partial Network Information |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.10547 |