  Data augmentation is essential when applying Machine Learning in small-data
regimes. It generates new samples following the observed data distribution
while increasing their diversity and variability to help researchers and
practitioners improve their models' robustness and, thus, deploy them in the
real world. Nevertheless, its usage in tabular data still needs to be improved,
as prior knowledge about the underlying data mechanism is seldom considered,
limiting the fidelity and diversity of the generated data. Causal data
augmentation strategies have been pointed out as a solution to handle these
challenges by relying on conditional independence encoded in a causal graph. In
this context, this paper experimentally analyzed the ADMG causal augmentation
method considering different settings to support researchers and practitioners
in understanding under which conditions prior knowledge helps generate new data
points and, consequently, enhances the robustness of their models. The results
highlighted that the studied method (a) is independent of the underlying model
mechanism, (b) requires a minimal number of observations that may be
challenging in a small-data regime to improve an ML model's accuracy, (c)
propagates outliers to the augmented set degrading the performance of the
model, and (d) is sensitive to its hyperparameter's value.
