Counterfactual explanations, engineered alterations of classifier inputs that change predictions, are widely used for interpreting classifier decisions. In this work, we extend counterfactual generation to point cloud data, addressing the challenges posed by its unstructured nature. We introduce hierarchical modeling to enhance counterfactual learning, specifically the ability to identify and manipulate key semantic traits influencing classifier predictions. Through visual and quantitative evaluations, we demonstrate the effectiveness of our method in generating counterfactuals that successfully reverse classifier predictions.