While the correlation analysis shows which movements take place in a correlated fashion, it is also interesting to evaluate the causality of these movements and how they propagate through the system. Given that tICA, a time-lag-based method, provided new insights into cage movement and product interaction, we applied the Granger Causality (GC) model, another time-lag-based approach, to assess the reaction trajectories for causal relationships. GC determines whether the prediction of a variable X in the future improves by including past events of Y. If this is the case, Y is found to Granger cause X. With this causality model, we analysed each trajectory separately. Therefore, we can assess which each feature is influenced by which of the other 48 features; however, a complete analysis of all features quickly becomes impractical. Having identified C-N bond formation and changes in Cu-coordination as the most critical factors during product formation, we focused our GC analysis on the C-N and Phena-Cu-Br features. For each of these two features, we analysed which of the remaining 48 features influenced them and counted how often each of the 48 variables was found to Granger-cause C-N and Phena-Cu-Br across all trajectories. For an overview over the 139 49x49 causality matrices, the reader is referred to the GitHub.