Counterfactual explanations are usually generated through heuristics that are
sensitive to the search's initial conditions. The absence of guarantees of
performance and robustness hinders trustworthiness. In this paper, we take a
disciplined approach towards counterfactual explanations for tree ensembles. We
advocate for a model-based search aiming at "optimal" explanations and propose
efficient mixed-integer programming approaches. We show that isolation forests
can be modeled within our framework to focus the search on plausible
explanations with a low outlier score. We provide comprehensive coverage of
additional constraints that model important objectives, heterogeneous data
types, structural constraints on the feature space, along with resource and
actionability restrictions. Our experimental analyses demonstrate that the
proposed search approach requires a computational effort that is orders of
magnitude smaller than previous mathematical programming algorithms. It scales
up to large data sets and tree ensembles, where it provides, within seconds,
systematic explanations grounded on well-defined models solved to optimality.

本文提出了一种基于模型的搜索方法，利用混合整数规划技术生成了对树集的对抗性解释，并通过隔离森林模型针对低异常得分的合理解释进行优化。