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There is a multiplicity of definitions of
enable
and
not-enable
and how
they might be applied. To some degree, logic notation definitional disputes
are involved. These issues are possibly germane to general causality theory.
However, it is not profitable to the interests of this paper to consider notational
issues; this paper is concerned with the less subtle needs of data analysis.
Negative causal relationships are less sure; but often stated; for example,
it is often said that:
Not walking under a ladder prevents bad luck.
Or, usually (but not always),
Stopping for a red light avoids an accident.
In summary, it can be said that the knowledge of at least some causal
effects is imprecise for both positive and negative descriptions. Perhaps, com-
plete knowledge of all possible factors might lead to a crisp description of
whether an effect will occur. However, it is also unlikely that it may be possi-
ble to fully know, with certainty, all of the elements involved. Consequently,
the extent or actuality of missing elements may not be known. Additionally,
some well described physics as well as neuro-biological events appear to be
truly random [5]; and some mathematical descriptions randomly uncertain. If
they are, there is no way of avoiding causal imprecision.
Coming to a precise description of what is meant by causality is di
cult.
There are multiple and sometimes conflicting definitions. For an introductory
discussion of these issues, see Mazlack [19]. Recognizing many things with ab-
solute certainty is problematic. As this is the case, our causal understanding
is based on a foundation of inherent uncertainty and incompleteness. Conse-
quently, causal reasoning models must accommodate inherent ambiguity. For
an introductory discussion of this, see Mazlack [17].
It may well be that a precise and complete knowledge of causal events is
not possible or at least uncertain. On the other hand, we have a commonsense
belief that causal effects exist in the real world. If we can develop models
tolerant of imprecision, it would be useful. Also, to some degree, the degree
of importance that some of these items have decreases as grain size increases.
2 Satisficing
People do things in the world by exploiting commonsense
perceptions
of cause
and effect. Manipulating perceptions has been explored [41] but is not the
focus of this paper. The interest here is how perceptions affect commonsense
causal reasoning, granularity, and the need for precision.
When trying to precisely reason about causality, complete knowledge of all
of the relevant events and circumstances is needed. In commonsense, every day
reasoning, approaches are used that do not require complete knowledge. Often,
approaches follow what is essentially a
satisficing
[37] paradigm. The use of
