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FACET: Robust Counterfactual Explanation Analytics

Summary: FACET is an explanation analytics system for counterfactuals of tree ensembles. It introduces counterfactual regions—robust, outcome-guaranteeing feature ranges—and a compact, high-dimensional index with index-aware queries for near real-time analytics; evaluated on eight datasets, it matches quality with an order-of-magnitude speedup and improves user understanding. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6806
Venue
SIGMOD
Year
2023
Pagerank
5.9850223e-05
Overall Rank
6,079 | 58.30%
DOI
10.1145/3626729

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{vannostrand_sigmod23,
        title = {{FACET: Robust Counterfactual Explanation Analytics}},
        author = {VanNostrand, Peter M. and Zhang, Huayi and Hofmann, Dennis M. and Rundensteiner, Elke A.},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3626729},
        url = {https://dl.acm.org/doi/10.1145/3626729},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
10,263 Local Stability of Rankings 2026 SIGMOD 5.093636e-05
10,725 Locator: Local Stability for Rankings 2025 SIGMOD 5.093636e-05
11,219 MetaStore: Analyzing Deep Learning Meta-Data at Scale 2024 VLDB 5.093636e-05
11,313 Counterfactual Explanation Analytics: Empowering Lay Users to Take Action Against Consequential Automated Decisions 2024 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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