A significant drawback of eXplainable Artificial Intelligence (XAI) approaches is the assumption of feature independence. This paper focuses on integrating causal knowledge in XAI methods to increase trust and help users assess explanations' quality. We propose a novel extension to a widely used local and model-agnostic explainer that explicitly encodes causal relationships in the data generated around the input instance to explain. Extensive experiments show that our method achieves superior performance comparing the initial one for both the fidelity in mimicking the black-box and the stability of the explanations.

翻译：摘要：可解释人工智能方法的一个显著缺陷是假设特征独立。本文聚焦于将因果知识融入XAI方法以提升信任度，并帮助用户评估解释质量。我们提出了一种对广泛使用的局部模型无关解释器的新扩展，该扩展显式编码了围绕待解释输入实例生成的数据中的因果关系。大量实验表明，与原始方法相比，我们的方法在模拟黑箱的保真度与解释稳定性方面均取得了更优性能。