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Suna: Scalable Causal Confounder Discovery over Relational Data

Summary: Suna: a GPU-optimized system that scalably discovers admissible confounder sets from relational repositories by exploiting cause–effect asymmetry to find unconfounded ancestors of treatments without a full causal diagram. Iterative, join-free algorithm achieves >100× speedups. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
14035
Venue
VLDB
Year
2025
Pagerank
4.1905499e-05
Overall Rank
10,732 | 25.42%
DOI
10.14778/3749646.3749684

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