Temporal data-to-text systems seem to acknowledge causal relations as important. However, they play a secondary role in their document planners and their identification relies mostly on domain knowledge. This thesis proposes an assisted temporal data interpretation model by narrative generation in which narratives are structured with the help of a mix of automatically mined and manually defined association rules. The structure of the narrative allows the reader to follow associations giving hints on causality from the beginning to the end of the text. An evaluation shows that the texts are understandable and the lexical choices adequate.