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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
h5983241881299e0e
Venue
SIGMOD
Year
2022
Pagerank
6.5111757e-05
Overall Rank
4,577 | 69.33%
DOI
10.1145/3514221.3526149
Incoming Non-self Citations Over Time
BibTeX Citation
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@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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