Redefining Centrality Measures in Weighted Causal Graphs

Causal graphs are powerful instruments for structuring information and analyzing the extent to which an observed effect can be attributed to a given cause. In this work, we demonstrate how edge-weighted causal graphs can be used to quantify whether a sentence acts as a direct or indirect cause. We introduce methods to identify the causal path with the highest total strength between two concepts and, given a specific context, to determine the pair of concepts with the strongest causal connection. In addition, we propose centrality measures that incorporate causality scores and graph weights, enabling the ranking of sentences by causal importance and identifying the most relevant ones.