Causal determination
Causal reasoning has been advocated as a central tenet of BBN modelling (Pearl
88). From this viewpoint the topology of a BBN is said to reflect the causal
structure of the real world and as such provides an explanatory framework for
inference and prediction. This interpretation of causality has its roots in Locke’s epistemological view of causality as a property of the world rather than
simply a way of describing the world. Here causation is not only a component of
experience but also an objective form of inter-dependence between objects and
events. This view contrasts with that of Hume which has enjoyed considerable
influence in modern science and holds that causation is a purely mental construct
where “causes” and “effects” are merely connected rather than produced one by the other. Philosophical
debates about the true nature of causality are outside the scope of this work but
the interested reader might wish to turn to (Bunge 79) for a detailed
discussion of the pros and cons of each school of thought. It is sufficient to say that
for our purposes the idea that causation as a property of the real world holds
some attraction since we are interested in predicting/explaining properties of
real systems.
Causal structuring in BBNs helps embody theories about how the world operates
as well as encoding uncertainties about its operation. Such a view contrasts
sharply with classical statistical analysis where correlation between variables
indicates mere phenomenological connection rather than causation. Any causal
explanation is then regarded as purely subjective. It is this key distinction that
gives BBNs the edge over statistical analysis. Structuring models using causal
ideas is more natural for the expert since descriptions of reasoning are
easily explained by comparison with reality. On the other hand in statistical
modelling statements about the world can often be hidden in mathematical parameters
which have no direct physical meaning. Likewise BBNs offer an advantage over
rule based expert systems in that causality offers different experts a common
medium to compare their reasoning. Rule based expert systems encode the mental
rules of the expert independently of whether these rules are consistent with the
real world.