Since artificial intelligence has seen tremendous recent successes in many areas, it has sparked great interest in its potential for trustworthy and interpretable risk prediction. However, most models lack causal reasoning and struggle with class imbalance, leading to poor precision and recall. To address this, we propose a Task-Driven Causal Feature Distillation model (TDCFD) to transform original feature values into causal feature attributions for the specific risk prediction task. The causal feature attribution helps describe how much contribution the value of this feature can make to the risk prediction result. After the causal feature distillation, a deep neural network is applied to produce trustworthy prediction results with causal interpretability and high precision/recall. We evaluate the performance of our TDCFD method on several synthetic and real datasets, and the results demonstrate its superiority over the state-of-the-art methods regarding precision, recall, interpretability, and causality.

翻译：自人工智能在多个领域取得显著成功以来，其在可信赖与可解释风险预测方面的潜力引发了广泛关注。然而，大多数模型缺乏因果推理能力且难以应对类别不平衡问题，导致精确率与召回率较低。为此，我们提出一种任务驱动的因果特征蒸馏模型（TDCFD），将原始特征值转化为针对特定风险预测任务的因果特征归因。该因果特征归因有助于描述特征值对风险预测结果的贡献程度。在完成因果特征蒸馏后，通过深度神经网络生成兼具因果可解释性与高精确率/召回率的可信预测结果。我们在多个合成数据集与真实数据集上评估了TDCFD方法的性能，结果表明其在精确率、召回率、可解释性及因果性方面均优于现有最先进方法。