Fair and Actionable Causal Prescription Ruleset
Summary: Fairness-aware causal prescription ruleset for actionable recommendations that improve outcomes without widening disparities between protected and non-protected groups. Uses causal reasoning and efficient optimization to search the rule space under fairness and coverage, with real data validation. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Benton Li (Cornell University)
- 2. Nativ Levy (Technion)
- 3. Brit Youngmann (Technion)
- 4. Sainyam Galhotra (Cornell University)
- 5. Sudeepa Roy (Duke University)
BibTeX Citation
@inproceedings{li_sigmod25,
title = {{Fair and Actionable Causal Prescription Ruleset}},
author = {Li, Benton and Levy, Nativ and Youngmann, Brit and Galhotra, Sainyam and Roy, Sudeepa},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725293},
url = {https://dl.acm.org/doi/10.1145/3725293},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,391 | Privacy-preserving and Verifiable Causal Prescriptive Analytics | 2026 | SIGMOD | 5.093636e-05 |
| 10,436 | Causal Explanations for Disparate Trends: Where and Why? | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 40 of 40 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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