Scalable Techniques for Mining Causal Structures
Summary: Scales causal-structure discovery in market-basket data without learning full Bayesian networks, addressing statistical and computational obstacles. Experiments show feasible mining and suggest ruling out causality is easier—and valuable for avoiding erroneous decisions. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Craig Silverstein (Stanford University)
- 2. Sergey Brin (Stanford University)
- 3. Rajeev Motwani (Stanford University)
- 4. Jeff Ullman (Stanford University)
BibTeX Citation
@article{silverstein_vldb98,
title = {{Scalable Techniques for Mining Causal Structures}},
author = {Silverstein, Craig and Brin, Sergey and Motwani, Rajeev and Ullman, Jeff},
journal = {PVLDB},
series = {{VLDB} '98},
pages = {594--605},
year = {1998}
}
Incoming Citations (Sorted by Pagerank)
Showing 11 of 11 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 13 | Mining Association Rules between Sets of Items in Large Databases | 1993 | SIGMOD | 0.00064420972 |
| 731 | Beyond Market Baskets: Generalizing Association Rules to Correlations | 1997 | SIGMOD | 0.00014394521 |
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