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)
Incoming Non-self Citations Over Time
Authors
- 1. Babak Salimi (University of Washington)
- 2. Luke Rodriguez (University of Washington)
- 3. Bill Howe (University of Washington)
- 4. Dan Suciu (University of Washington)
BibTeX Citation
@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}
}Incoming Citations (Sorted by Pagerank)
Showing 43 of 43 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,314 | Computing Optimal Repairs for Functional Dependencies | 2018 | PODS | 0.000110547 |
| 2,361 | Bias in OLAP Queries: Detection, Explanation, and Removal (Or Think Twice About Your AVG-Query) | 2018 | SIGMOD | 8.5605216e-05 |
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