We aimed to infer the cause of the onset of the chemical reaction. To this end, we focused our investigation on the educt and TS ensembles of each trajectory. We eliminated trajectories with few educt structures (less than 100), thus having a total of 139 reaction sampling events. To infer Granger Causality 68 (GC) we followed the protocol outlined by Toda and Yamamoto. This involved performing the augmented Dickey-Fuller 95 and Kwiatkowski-Phillips-Schmidt-Shin 96 tests to ascertain the stationarity of the various time series. Most of the time series were deemed stationary, with only a couple of the features presenting non-stationarity, which were rendered stationary through differencing (see GitHub for the critical and test statistics for each test and trajectory). A multivariate vector autoregression model was constructed and fitted with lag times varying from 0 to 50, for each time series. The appropriate lag time was chosen for each trajectory, based on the Akaike information criterion . The correlated time series were checked for cointegration using the Johansen test. Finally, we calculated a GC matrix for each feature, for every simulation, resulting in a total of 139 matrices, using Vector Autoregressive (VAR) models trained at the appropriate lag time. To account for the occurrence of false positives with repeated sampling of the reaction, we applied the false discovery rate (FDR) correction proposed by Benjamini and Hochberg, 102 with a threshold alpha of 0.1. The threshold for the GC test was set to p < 0.05. The full pvalues for the causality matrices, non-FDR corrected, can be found on GitHub, along with the results of all statistical tests.