Recognizing and developing causal relationships is essential for reasoning; it forms the basis for acting intelligently in the world. Causal knowledge provides a deep understanding of a system; and, the potential control over a system that comes from being able to predict action's consequences. Knowledge of at least some relationships is inherently imprecise. Causal complexes are groupings of smaller causal relations that can make up a larger-grained causal object. Usually, commonsense reasoning is more successful in reasoning about a few large-grained events than many finer-grained events. However, larger-grained causal objects are necessarily more imprecise. Often, a network represents a causal relationship with conditioned edges (probability, possibility, randomness, etc.). Various representational graphs and models can be used. Needed necessary descriptions include cycles, including mutual dependencies, both with non-cumulative effects and cumulative effects. Without cyclic descriptions, there would be an incomplete representation of the variety and wealth of causal constructions used in science as well as in everyday life. Directed Bayesian causal networks have received significant attention; they have a significant weakness in that they do not allow cycles; they have other significant restrictions, including Markoff independence conditions. This paper discusses: causality, complexes, granularity, imprecision, general and Bayesian causal models; the perspective reflects data mining goals.
['Lawrence J. Mazlack']
General imprecise causal reasoning representations