The Missing Link: Allocation Performance in Causal Machine Learning
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913431443996672 |
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| author | Fischer-Abaigar, Unai Kern, Christoph Kreuter, Frauke |
| author_facet | Fischer-Abaigar, Unai Kern, Christoph Kreuter, Frauke |
| contents | Automated decision-making (ADM) systems are being deployed across a diverse range of critical problem areas such as social welfare and healthcare. Recent work highlights the importance of causal ML models in ADM systems, but implementing them in complex social environments poses significant challenges. Research on how these challenges impact the performance in specific downstream decision-making tasks is limited. Addressing this gap, we make use of a comprehensive real-world dataset of jobseekers to illustrate how the performance of a single CATE model can vary significantly across different decision-making scenarios and highlight the differential influence of challenges such as distribution shifts on predictions and allocations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10779 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Missing Link: Allocation Performance in Causal Machine Learning Fischer-Abaigar, Unai Kern, Christoph Kreuter, Frauke Machine Learning Computers and Society Methodology Automated decision-making (ADM) systems are being deployed across a diverse range of critical problem areas such as social welfare and healthcare. Recent work highlights the importance of causal ML models in ADM systems, but implementing them in complex social environments poses significant challenges. Research on how these challenges impact the performance in specific downstream decision-making tasks is limited. Addressing this gap, we make use of a comprehensive real-world dataset of jobseekers to illustrate how the performance of a single CATE model can vary significantly across different decision-making scenarios and highlight the differential influence of challenges such as distribution shifts on predictions and allocations. |
| title | The Missing Link: Allocation Performance in Causal Machine Learning |
| topic | Machine Learning Computers and Society Methodology |
| url | https://arxiv.org/abs/2407.10779 |