Is merging worth it? Securely evaluating the information gain for causal dataset acquisition
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911034314326016 |
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| author | Fawkes, Jake Ter-Minassian, Lucile Ivanova, Desi Shalit, Uri Holmes, Chris |
| author_facet | Fawkes, Jake Ter-Minassian, Lucile Ivanova, Desi Shalit, Uri Holmes, Chris |
| contents | Merging datasets across institutions is a lengthy and costly procedure, especially when it involves private information. Data hosts may therefore want to prospectively gauge which datasets are most beneficial to merge with, without revealing sensitive information. For causal estimation this is particularly challenging as the value of a merge depends not only on reduction in epistemic uncertainty but also on improvement in overlap. To address this challenge, we introduce the first cryptographically secure information-theoretic approach for quantifying the value of a merge in the context of heterogeneous treatment effect estimation. We do this by evaluating the Expected Information Gain (EIG) using multi-party computation to ensure that no raw data is revealed. We further demonstrate that our approach can be combined with differential privacy (DP) to meet arbitrary privacy requirements whilst preserving more accurate computation compared to DP alone. To the best of our knowledge, this work presents the first privacy-preserving method for dataset acquisition tailored to causal estimation. We demonstrate the effectiveness and reliability of our method on a range of simulated and realistic benchmarks. Code is publicly available: https://github.com/LucileTerminassian/causal_prospective_merge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07215 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Is merging worth it? Securely evaluating the information gain for causal dataset acquisition Fawkes, Jake Ter-Minassian, Lucile Ivanova, Desi Shalit, Uri Holmes, Chris Machine Learning Cryptography and Security Merging datasets across institutions is a lengthy and costly procedure, especially when it involves private information. Data hosts may therefore want to prospectively gauge which datasets are most beneficial to merge with, without revealing sensitive information. For causal estimation this is particularly challenging as the value of a merge depends not only on reduction in epistemic uncertainty but also on improvement in overlap. To address this challenge, we introduce the first cryptographically secure information-theoretic approach for quantifying the value of a merge in the context of heterogeneous treatment effect estimation. We do this by evaluating the Expected Information Gain (EIG) using multi-party computation to ensure that no raw data is revealed. We further demonstrate that our approach can be combined with differential privacy (DP) to meet arbitrary privacy requirements whilst preserving more accurate computation compared to DP alone. To the best of our knowledge, this work presents the first privacy-preserving method for dataset acquisition tailored to causal estimation. We demonstrate the effectiveness and reliability of our method on a range of simulated and realistic benchmarks. Code is publicly available: https://github.com/LucileTerminassian/causal_prospective_merge. |
| title | Is merging worth it? Securely evaluating the information gain for causal dataset acquisition |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2409.07215 |