Counterfactual explanation is an important Explainable AI technique to
explain machine learning predictions. Despite being studied actively, existing
optimization-based methods often assume that the underlying machine-learning
model is differentiable and treat categorical attributes as continuous ones,
which restricts their real-world applications when categorical attributes have
many different values or the model is non-differentiable. To make
counterfactual explanation suitable for real-world applications, we propose a
novel framework of Model-Agnostic Counterfactual Explanation (MACE), which
adopts a newly designed pipeline that can efficiently handle non-differentiable
machine-learning models on a large number of feature values. in our MACE
approach, we propose a novel RL-based method for finding good counterfactual
examples and a gradient-less descent method for improving proximity.
Experiments on public datasets validate the effectiveness with better validity,
sparsity and proximity.

提出了一种新的框架 Model-Agnostic Counterfactual Explanation (MACE)，通过采用一种新设计的流水线来有效处理大量特征值上的非可分机器学习模型，其中包括使用基于 RL 的方法来找到良好的对策例子和梯度下降方法来改进相似性。公共数据集上的实验证实了该方法的有效性，具有更好的准确性、稀疏性和相似性。