Another aspect that has until now been neglected in chemistry is causality. While the concept is widespread across various scientific domains -ranging from economics and climate research to biology and medical studies -it remains surprisingly absent in the field of chemistry. Although a handful of precedents in biomolecular simulations exists, it has not been explored to study chemical reactions, not to mention transition-metal catalysis. As MD simulation trajectories are essentially discrete time series, containing the various degrees of freedom of the system, causality can be statistically inferred from the analysis of these trajectories. Consequently, the reaction coordinate can be decomposed into a sequence of motions leading up to the reaction, exposing the intricate interplay of functional groups of the system, offering an unprecedented view of reactivity. A supramolecular catalyst that has shown remarkable catalytic efficacy for C-N coupling is the Cu(I)-1,5-(2,9-dimethyl-1,10-phenanthroyl)-2,3,4,6,7,8-hexamethyl-p-tert-butylcalix [8]arene, short noted as [Cu(C8PhenMe6)I]. The macrocyclic ligand allows for usage of earth abundant metals, here Cu, which is an essential step towards more sustainable chemical processes. The investigated system necessitates explicit solvation for accurate results, as implicit solvation models lead to a collapse of the macrocyclic cage, which compromises catalytic activity. To account for the conformational flexibility of the supramolecular cage and the dynamic nature of the system as a whole, a dynamic, ensemble-based approach is required to study the reaction. While the mechanism of this catalyst was established to be a sequence of oxidative addition / reductive elimination, the dynamic effects of the system, in particular the contribution of the cage, are unknown. From previous studies, we know that explicit solvent molecules are crucial to maintain the shape and functionality of the cavity, however we see no experimental or theoretical evidence of their participation in the reaction itself. We developed a multiscale QM/MM MD approach to understand the bond formation dynamics of the C-N coupling step with the Cu-calix [8]arene catalyst in explicit chloroform. By relying on the GFN2-xTB 67 method to describe the QM part, we achieved massive sampling, resulting in 152 individual unbiased reaction trajectories. To extract chemically relevant information from these data, we employed supervised and unsupervised interpretable machine learning dimensionality reduction models, in order to identify the cumulative reaction coordinate and to detect critical movements in the structure. A consensus approach combining individual machine learning techniques improved the performance and we employed the statistical Granger Causality analysis model to decompose the reaction coordinate into a sequence of individual consecutive movements. Finally, Random Forest models and Decision Rules allowed us to quantify the reaction coordinate. This work serves as a broadly applicable template for any mechanistic investigation, revealing and quantifying complex reaction coordinates, along with causal effects derived from the individual movements leading up to the reaction.