  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.
