Uncertainty Weighted Causal Graphs
This work addresses the challenge of handling uncertainty in causal graphs for researchers or practitioners in fields like data science or knowledge representation, but it appears incremental as it builds on prior methods for generating such graphs.
The authors tackled the problem of modeling uncertainty in automatically generated causal graphs, which often contain imperfect causal information, by introducing probabilistic methods to improve the management of imprecision in these graphs.
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.