Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Scalable Techniques for Mining Causal Structures
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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Causal reasoning occupies a central position in human reasoning. In order to algorithmically consider causal relations, the relations must be placed into a representation that supports manipulation. The most widespread causal representation in current usage is directed acyclic graphs. However, they are severely limited in what portion of the every day world they can represent. Some of the required Markov conditions do not fit with commonsense reasoning. More importantly, cycles must be represented and they cannot be represented in acyclic graphs. Additionally, shifts in grain size are overly limited. Commonsense understanding deals with imprecision, uncertainty and imperfect knowledge. An algorithmic way of handling and representing causal imprecision that includes cycles is needed.