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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.14549 |
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| _version_ | 1866914653431398400 |
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| author | Yao, Leon Li, Paul Yiming Lu, Jiannan |
| author_facet | Yao, Leon Li, Paul Yiming Lu, Jiannan |
| contents | In accordance with the principle of "data minimization", many internet companies are opting to record less data. However, this is often at odds with A/B testing efficacy. For experiments with units with multiple observations, one popular data minimizing technique is to aggregate data for each unit. However, exact quantile estimation requires the full observation-level data. In this paper, we develop a method for approximate Quantile Treatment Effect (QTE) analysis using histogram aggregation. In addition, we can also achieve formal privacy guarantees using differential privacy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14549 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Privacy-preserving Quantile Treatment Effect Estimation for Randomized Controlled Trials Yao, Leon Li, Paul Yiming Lu, Jiannan Methodology In accordance with the principle of "data minimization", many internet companies are opting to record less data. However, this is often at odds with A/B testing efficacy. For experiments with units with multiple observations, one popular data minimizing technique is to aggregate data for each unit. However, exact quantile estimation requires the full observation-level data. In this paper, we develop a method for approximate Quantile Treatment Effect (QTE) analysis using histogram aggregation. In addition, we can also achieve formal privacy guarantees using differential privacy. |
| title | Privacy-preserving Quantile Treatment Effect Estimation for Randomized Controlled Trials |
| topic | Methodology |
| url | https://arxiv.org/abs/2401.14549 |