Semantic Meaningfulness: Evaluating Counterfactual Approaches for Real-World Plausibility and Feasibility

Counterfactual explanations are rising in popularity when aiming to increase the explainability of machine learning models. This type of explanation is straightforward to understand and provides actionable feedback (i.e., how to change the model decision). One of the main challenges that remains is generating meaningful counterfactuals that are coherent with real-world relations. Multiple approaches incorporating real-world relations have been proposed in the past, e.g. by utilizing data distributions or structural causal models. However, evaluating whether the explanations from different counterfactual approaches fulfill known causal relationships is still an open issue. To fill this gap, this work proposes two metrics - Semantic Meaningful Output (SMO) and Semantic Meaningful Relations (SMR) - to measure the ability of counterfactual generation approaches to depict real-world relations. In addition, we provide multiple datasets with known structural causal models and leverage them to benchmark the semantic meaningfulness of new and existing counterfactual approaches. Finally, we evaluate the semantic meaningfulness of nine well-established counterfactual explanation approaches and conclude that none of the non-causal approaches were able to create semantically meaningful counterfactuals consistently.