Data Mining & Possibly Imprecise Granular Causal Complexes

Causal reasoning occupies a central position in human reasoning, particularly in decision-making. Causal relationships found from mining data have a high decision value. When events happen, there are usually related events. The entire collection of events is called a complex. Complexes group finer-grained elements into larger-grained elements. Complete knowledge of all possible causal factors could lead to a crisp causal understanding. However, knowledge of at least some causal factors is inherently imprecise. It is unlikely that complete knowledge of all possible factors can be known for many subjects. What events are in the complex may not be known; nor, what constraints and laws the complex is subject to. Consequently, causal knowledge is often incomplete. Whether or not all of the elements are precisely known, commonsense understanding recognizes that a complex of elements may cause an effect. Commonsense perception of causality is often large-grained even though the underlying causal structures may be fine-grained. Usually, common-sense reasoning is more successful in reasoning about a few larger-grained events than many finer-grained events. However, the larger-grained objects are necessarily more imprecise than some of their components. A satisfying solution might be to develop large-grained solutions and only use finer-grained information when the impreciseness of the large-grain is unsatisfactory