  Causality has traditionally been a scientific way to generate knowledge by
relating causes to effects. From an imaginery point of view, causal graphs are
a helpful tool for representing and infering new causal information. In
previous works, we have generated automatically causal graphs associated to a
given concept by analyzing sets of documents and extracting and representing
the found causal information in that visual way. The retrieved information
shows that causality is frequently imperfect rather than exact, feature
gathered by the graph. In this work we will attempt to go a step further
modelling the uncertainty in the graph through probabilistic improving the
management of the imprecision in the quoted graph.
