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Causal Search for Skylines (CSS): Causally-Informed Selective Data De-Correlation

Summary: Uses causal graphs to selectively de-correlate skyline preference attributes: remove correlations misaligned with preferences while preserving beneficial ones. CSS is agnostic to enumeration/pruning algorithms and reduces dominance checks and runtime across datasets. (summarized by gpt-5.6-luna on Jul 26 2026)

Paper ID
7399
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,208 | 29.97%
DOI
10.1145/3802026

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Authors

BibTeX Citation

@inproceedings{mandal_sigmod26,
        title = {{Causal Search for Skylines (CSS): Causally-Informed Selective Data De-Correlation}},
        author = {Mandal, Pratanu and Gorantla, Abhinav and Candan, K. Selçuk and Sapino, Maria Luisa},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3802026},
        url = {https://dl.acm.org/doi/10.1145/3802026},
        year = {2026}
}

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