Using association rules without understanding their underlying causality reduces their decision value

Association rules are a data mining cornerstone. Association rules greatest impact is in helping to make decisions. One association rule quality measure is a rule´s decision value. Association rules are often constructed using simplifying assumptions that lead to naive results and consequently naive decisions. Perhaps the greatest impact on decision value quality comes from treating association rules as causal statements without understanding whether there is, in fact, underlying causality. Complete knowledge of all possible factors might lead to a crisp description of whether an effect will occur. However, it is unlikely that all possible factors can be known. Commonsense world understanding accepts imprecision, uncertainty and imperfect knowledge. People recognize that a complex collection of elements can cause a particular effect. The events in the complex may not be known; or, what constraints and laws the complex is subject to. Sometimes, the details underlying an event can be known to a fine level of detail, sometimes not. Usually, commonsense reasoning is more successful in reasoning about a few large-grain sized events than many fine-grained events. A satisficing solution would be to develop large-grained solutions and only use the flner- grain when the impreciseness of the large-grain is unsatisfactory.