Causal graphs are a useful notation to represent the interplay between the actors as well as the polarity and strength of the relationship that they share. They are used extensively in educational, professional, and industrial contexts to simulate different scenarios, validate behavioral aspects, visualize the connections between different processes, and explore the adversarial effects of changing certain nodes. However, as the size of the causal graphs increase, interpreting them also becomes increasingly tougher. In such cases, new analytical tools are required to enhance the user's comprehension of the graph, both in terms of correctness and speed. To this purpose, this thesis introduces 1) a system that allows for causal exploration of directed graphs, while enabling the user to see the effect of interventions on the target nodes, 2) the use of natural language generation techniques to create a coherent passage explaining the propagation effects, and 3) results of an expert user study validating the efficacy of the narratives in enhancing the user's understanding of the causal graphs. In overall, the system aims to enhance user experience and promote further causal exploration.