While the GC model has been shown to be a reliable approach for identifying causality, it suffers from some inherent limitations. The model requires the dataset to be presented in the form of time-series with constant variance and mean (stationary). To this end, the time-series stationarity must be verified and, depending on the results, rendered stationary through common statistical approaches such as differentiation. Another limitation in the nature of the GC analysis is the influence of hidden variables that may not have been taken into account in the initial dataset. This implies that the dataset must be carefully chosen, either manually curated or through automated means, such as feature selection. Moreover, the GC test also looks at first degree causality between the selected features, meaning that while we observe one variable influencing another, we cannot rule out that the influencing variable was not provoked by a 2 nd variable beforehand. This can be overcome by performing a GC analysis on each possible pair of features and interpreting the results; however, this becomes difficult as the number of important features increases.