Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
The Art of Causal Conjecture
Data Mining and Knowledge Discovery
Knowledge Discovery from Telecommunication Network Alarm Databases
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Scalable Techniques for Mining Causal Structures
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A characterization of the dirichlet distribution with application to learning Bayesian networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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Causal reasoning is important to human reasoning. It plays an essential role in day-today human decision-making. Human understanding of causality is necessarily imprecise, imperfect, and uncertain. Soft computing methods may be able to provide the approximation tools needed. In order to algorithmically consider causes, imprecise causal models are needed. A difficulty is striking a good balance between precise formalism and imprecise reality. Determining causes from available data has been a goal throughout human history. Today, data mining holds the promise of extracting unsuspected information from very large databases. The most common methods build rules. In many ways, the interest in rules is that they offer the promise (or illusion) of causal, or at least, predictive relationships. However, the most common rule form (association rules) only calculates a joint occurrence frequency; they do not express a causal relationship. If causal relationships could be discovered, it would be very useful.