Causal satisficing and Markoff models in the context of data mining

Causal reasoning occupies a central position in human reasoning. In many ways, causality is granular. This is true for commonsense reasoning as well as for mathematical and scientific theory. Knowledge of at least some causal effects is imprecise. Perhaps, complete knowledge of all possible factors might lead to a crisp description of whether an effect will occur. However, in our commonsense world, it is unlikely that all possible factors can be known. In commonsense, every day reasoning, we use approaches that do not require complete knowledge. We need an algorithmic way of handling imprecision if we are to computationally handle causality. Perhaps, fuzzy Markoff models might be useful. People recognize that a complex collection of elements causes a particular effect, even if the precise elements of the complex are unknown. Perhaps Markoff chains might be used to build these complexes. It may be more useful to work on a larger grain size. This may reduce the need to learn extensive hidden Markoff models, which in computationally expensive. Perhaps, a satisficing solution would be to develop large grained solutions and then only go to the finer grain when the impreciseness of the large grain is unsatisfactory.