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HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach

Summary: HYPER enables what-if and how-to hypothetical reasoning over data with probabilistic causal dependencies, capturing cross-attribute effects from updates. Extending SQL with new operators, it defines semantics and algorithms grounded in causality and probabilistic databases, with optimizations and experimental evaluation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6523
Venue
SIGMOD
Year
2022
Pagerank
6.3974766e-05
Overall Rank
5,025 | 65.53%
DOI
10.1145/3514221.3526149

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{galhotra_sigmod22,
        title = {{HYPER: Hypothetical Reasoning With What-If and How-To Queries Using a Probabilistic Causal Approach}},
        author = {Galhotra, Sainyam and Gilad, Amir and Roy, Sudeepa and Salimi, Babak},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3526149},
        url = {https://dl.acm.org/doi/10.1145/3514221.3526149},
        year = {2022}
}

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