Feature attribution methods are widely used to explain neural models by
determining the influence of individual input features on the models' outputs.
We propose a novel feature attribution method, CAFE (Conflict-Aware
Feature-wise Explanations), that addresses three limitations of the existing
methods: their disregard for the impact of conflicting features, their lack of
consideration for the influence of bias terms, and an overly high sensitivity
to local variations in the underpinning activation functions. Unlike other
methods, CAFE provides safeguards against overestimating the effects of neuron
inputs and separately traces positive and negative influences of input features
and biases, resulting in enhanced robustness and increased ability to surface
feature conflicts. We show experimentally that CAFE is better able to identify
conflicting features on synthetic tabular data and exhibits the best overall
fidelity on several real-world tabular datasets, while being highly
computationally efficient.

我们提出了一种新颖的特征归因方法 CAFE（冲突感知特征解释），它解决了现有方法中的三个限制：它们忽视冲突特征的影响，不考虑偏差项的影响，以及对基础激活函数的局部变化过于敏感。与其他方法不同，CAFE 在溯源神经元输入的影响上提供了防止效应被高估的保护措施，并单独跟踪输入特征和偏差的正负影响，从而增强了鲁棒性，并增加了表面冲突特征的能力。我们的实验证明，CAFE 能够更好地识别合成表格数据中的冲突特征，并在几个真实世界的表格数据集上具有最好的整体保真度，同时具有很高的计算效率。