Reproducibility Report for ACM SIGMOD 2024 Paper: 'FACET: Robust Counterfactual Explanation Analytics'
We are able to faithfully reproduce the original paper's findings as well as the key results reported in its experimental section (i.e., explanation quality, speed, robustness). The authors provided example data, comprehensive scripts, and plotting functions that allowed near-identical reconstruction of all of the paper's figures.
Authors
- Wan Shen Lim (ORCID: https://orcid.org/0000-0003-1508-2080)
- Peter M. VanNostrand (ORCID: https://orcid.org/0000-0002-0285-6019)
- Konstantinos Kanellis (ORCID: https://orcid.org/0009-0006-3776-3863)
- Donghyun Sohn (ORCID: https://orcid.org/0009-0001-6237-5399)
Institutions
- Worcester Polytechnic Institute (US)
- Northwestern University (US)
- University of Wisconsin–Madison (US)
- Carnegie Mellon University (US)
Publication Details
- Published
- 2025-03-21
- DOI
- https://doi.org/10.1145/3687998.3717053
- Primary Topic
- Explainable Artificial Intelligence (XAI)
- Type
- article
- Field-Weighted Citation Impact
- 0.00