Model interpretability plays a central role in human-AI decision-making
systems. Ideally, explanations should be expressed using human-interpretable
semantic concepts. Moreover, the causal relations between these concepts should
be captured by the explainer to allow for reasoning about the explanations.
Lastly, explanation methods should be efficient and not compromise the
performance of the predictive task. Despite the rapid advances in AI
explainability in recent years, as far as we know to date, no method fulfills
these three properties. Indeed, mainstream methods for local concept
explainability do not produce causal explanations and incur a trade-off between
explainability and prediction performance. We present DiConStruct, an
explanation method that is both concept-based and causal, with the goal of
creating more interpretable local explanations in the form of structural causal
models and concept attributions. Our explainer works as a distillation model to
any black-box machine learning model by approximating its predictions while
producing the respective explanations. Because of this, DiConStruct generates
explanations efficiently while not impacting the black-box prediction task. We
validate our method on an image dataset and a tabular dataset, showing that
DiConStruct approximates the black-box models with higher fidelity than other
concept explainability baselines, while providing explanations that include the
causal relations between the concepts.

DiConStruct 是一种基于概念和因果关系的解释方法，目的是通过结构性因果模型和概念归因来创建更可解释的局部解释，并有效地近似黑盒机器学习模型的预测，而不影响预测任务。