Abstract
We propose a novel training regime termed counterfactual training that leverages counterfactual explanations to increase the explanatory capacity of models. Counterfactual explanations have emerged as a popular post-hoc explanation method for opaque machine learning models: they inform how factual inputs would need to change in order for a model to produce some desired output. To be useful in real-world decision-making systems, counterfactuals should be plausible with respect to the underlying data and actionable with respect to the feature mutability constraints. Much existing research has therefore focused on developing post-hoc methods to generate counterfactuals that meet these desiderata. In this work, we instead hold models directly accountable for the desired end goal: counterfactual training employs counterfactuals during the training phase to minimize the divergence between learned representations and plausible, actionable explanations. We demonstrate empirically and theoretically that our proposed method facilitates training models that deliver inherently desirable counterfactual explanations and additionally exhibit improved adversarial robustness.
Abstract (translated)
我们提出了一种新的训练方法，称为反事实训练（counterfactual training），该方法利用反事实解释来增强模型的解释能力。反事实解释作为一种流行的事后解释方法已经为不透明的机器学习模型广泛使用：它们提供关于现实输入如何需要改变才能使模型产生所需输出的信息。为了在实际决策系统中发挥作用，反事实应该与底层数据相符，并且在特征可变性约束下具有操作性。因此，现有的许多研究都集中在开发能够生成符合这些标准的事后方法上。 然而，在这项工作中，我们直接让模型对其期望的目标负责：反事实训练通过在训练阶段使用反事实来最小化学习表示与合理、可行的解释之间的差异。我们从实证和理论上证明了所提出的方法有助于训练出自然提供具有内在价值的反事实解释的模型，并且这些模型还表现出改进后的对抗鲁棒性。
URL
https://arxiv.org/abs/2601.16205
