Counterfactual explanations elucidate algorithmic decisions by pointing to
scenarios that would have led to an alternative, desired outcome. Giving
insight into the model's behavior, they hint users towards possible actions and
give grounds for contesting decisions. As a crucial factor in achieving these
goals, counterfactuals must be plausible, i.e., describing realistic
alternative scenarios within the data manifold. This paper leverages a recently
developed generative modeling technique -- adversarial random forests (ARFs) --
to efficiently generate plausible counterfactuals in a model-agnostic way. ARFs
can serve as a plausibility measure or directly generate counterfactual
explanations. Our ARF-based approach surpasses the limitations of existing
methods that aim to generate plausible counterfactual explanations: It is easy
to train and computationally highly efficient, handles continuous and
categorical data naturally, and allows integrating additional desiderata such
as sparsity in a straightforward manner.

利用对抗式随机森林（ARFs）的生成建模技术，本文以模型独立的方式高效生成可信的反事实解释，克服了现有方法的局限性，包括易于训练、计算高效、自然处理连续和分类数据以及方便集成额外的期望，如稀疏性。