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VLDB
1998
ACM

Scalable Techniques for Mining Causal Structures

13 years 7 months ago
Scalable Techniques for Mining Causal Structures
Mining for association rules in market basket data has proved a fruitful areaof research. Measures such as conditional probability (confidence) and correlation have been used to infer rules of the form "the existence of item A implies the existence of item B." However, such rules indicate only a statistical relationship between A and B. They do not specify the nature of the relationship: whether the presence of A causes the presence of B, or the converse, or some other attribute or phenomenon causes both to appear together. In applications, knowing such causal relationships is extremely useful for enhancing understanding and effecting change. While distinguishing causality from correlation is a truly difficult problem, recent work in statistics and Bayesian learning provide some avenues of attack. In these fields, the goal has generally been to learn complete causal models, which are essentially impossible to learn in large-scale data mining applications with a large number ...
Craig Silverstein, Sergey Brin, Rajeev Motwani, Je
Added 06 Aug 2010
Updated 06 Aug 2010
Type Conference
Year 1998
Where VLDB
Authors Craig Silverstein, Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman
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