Databases Reference
In-Depth Information
1.1 Granularity
Causality is often granular. This is true both with commonsense reasoning as
well as for more formal mathematical and scientific approaches. At a very fine-
grained level, the physical world itself may be granular [31]. Commonsense
perceptions of causality are often large-grained while the underlying causal
structures may be fine-grained.
Larger-grained causal objects are often more imprecise than some of the
components that are collected into the larger-grained object. Some compo-
nents of a larger-grained causal object may be precisely known, while others
maybe be somewhat imprecise, and others unknown. The larger the grain, the
greater is the likelihood that there might be missing or unknown components.
How to evaluate the impreciseness of a larger-grained causal object when the
impreciseness of the underlying cascade of components is not clear. Perhaps,
some form of type-II fuzzy [17] manipulation might be helpful.
1.2 Reasoning
Commonsense understanding of the world tells us that we have to deal with
imprecision, uncertainty and imperfect knowledge. This is also the case with
scientific knowledge of the world. A di
culty is striking a good balance be-
tween precise formalism and commonsense imprecise reality.
Causal relationships exist in the commonsense world; for example:
When a glass is pushed off a table and breaks on the floor
it might be said that
Being pushed from the table
caused
the glass to break.
Although,
Being pushed from a table is not a
certain
cause
of breakage; some-
times the glass bounces and no break occurs; or, someone catches the
glass before it hits the floor.
Counterfactually, usually (but not always),
Not
falling to the floor prevents breakage.
Sometimes,
A glass breaks when an errant object hits it, even though it does not
fall from the table.
Positive
causal relationships can be described as:
if α then β
(or,
α
→
β
).
For example:
