CFDB: Machine Learning Model Analysis via Databases of CounterFactuals
Summary: CFDB provides a relational, queryable database of counterfactuals to unify CF generation, selection, and analysis for evolving models. With multi-level abstractions, it supports local explanations and global model insights on Lending Club loan data. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Idan Meyuhas (Tel Aviv University)
- 2. Aviv Ben Arie (Intuit)
- 3. Yair Horesh (Intuit)
- 4. Daniel Deutch (Tel Aviv University)
BibTeX Citation
@inproceedings{meyuhas_sigmod22,
title = {{CFDB: Machine Learning Model Analysis via Databases of CounterFactuals}},
author = {Meyuhas, Idan and Arie, Aviv Ben and Horesh, Yair and Deutch, Daniel},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3520162},
url = {https://dl.acm.org/doi/10.1145/3514221.3520162},
year = {2022}
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,000 | Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals | 2021 | SIGMOD | 7.8677069e-05 |
| 4,571 | GeCo: Quality Counterfactual Explanations in Real Time | 2021 | VLDB | 6.625099e-05 |
| 11,828 | Personal Insights for Altering Decisions of Tree-based Ensembles over Time | 2020 | VLDB | 5.093636e-05 |
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