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Interventional Fairness : Causal Database Repair for Algorithmic Fairness
Summary: Introduces interventional fairness by framing algorithmic fairness as a causal database repair problem. Shows sufficient conditions via admissible variables (not full causal models), develops repair algorithms with provable fairness guarantees, and demonstrates improvements on real data.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
5775
Venue
SIGMOD
Year
2019
Pagerank
0.00013531835
Overall Rank
863 | 94.09%
DOI
10.1145/3299869.3319901
Incoming Non-self Citations Over Time
BibTeX Citation
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@inproceedings{salimi_sigmod19,
title = {{Interventional Fairness : Causal Database Repair for Algorithmic Fairness}},
author = {Salimi, Babak and Rodriguez, Luke and Howe, Bill and Suciu, Dan},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3319901},
url = {https://dl.acm.org/doi/10.1145/3299869.3319901},
year = {2019}
}
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