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.

我们提出了一种基于任务驱动的因果特征蒸馏（TDCFD）模型，将原始特征值转化为特定风险预测任务的因果特征归因，通过因果特征蒸馏和深度神经网络的结合，可产生具有因果解释力、高精确度/召回率以及可靠性的预测结果。

面向可靠风险预测的任务驱动因果特征蒸馏