Counterfactual instances alone provide little explanation for machine learning decisions.
The article argues that just providing counterfactual instances is not enough to explain machine learning decisions. It suggests that a good explanation should include both counterfactual instances and causal equations. By combining these two elements, explainable AI methods can effectively clarify how machine learning systems make predictions.