One potential drawback of using counterfactuals in model training is the risk of overfitting to specific scenarios created by the transformations. If too much emphasis is placed on generating counterfactuals for rare or extreme cases, the model may become overly specialized and perform poorly on general data distributions. Another limitation is related to interpretability - while counterfactuals provide valuable insights into model behavior and feature importance, interpreting these results accurately requires domain knowledge and expertise. Without a deep understanding of the underlying data and context, misinterpretations or incorrect conclusions may arise from analyzing counterfactual scenarios.