Researchers propose a unified evaluation protocol for robust counterfactual explanations across different types of model changes, including parameter perturbations, retraining on new data, and new architectures. They evaluate six robust methods and two standard baselines on four tabular datasets, finding that relative performance and failure modes vary across change families.