  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.
