发表机构
Georgia Institute of Technology; Vector Institute; Drexel University; Leiden University（佐治亚理工学院; 向量研究院; 德雷塞尔大学; 莱顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对算法追索仅关注翻转模型预测的缺陷，提出因果表演框架建模追索行动的因果传播，实验显示其优于标准方法并减少模型重训需求。
AI 中文摘要
算法追索旨在为高风险分类场景（如贷款和抵押贷款申请）中的个人提供可操作的改变，以改善其预测结果。然而，大多数现有方法仅关注翻转模型的预测，未考虑推荐的改变是否会真正提升个人的真实资质，或仅仅是使其能够策略性地操纵分类器。因此，已部署的追索策略可能会引发行为反应，降低预测准确性，并在模型重新训练后失效。在本研究中，我们通过一种用于追索的因果表演框架将这种失败模式形式化。我们对追索行动如何通过结构因果模型传播进行建模，捕捉特征之间的相互作用及其对真实标签的影响。即使在标准凸损失下，这些因果响应也会引发非凸优化问题。我们确定了表演稳定解存在的条件，且可通过简单的迭代动力学高效计算。我们的分析表明，忽略因果结构的追索策略可能会引发巨大的、错位的行为反应，而因果追索则会产生稳定的均衡，减少操纵动机。在半合成和真实信用数据集上的实验表明，我们的方法始终优于标准经验风险最小化，同时减少了因策略性主体行为导致分布变化而需要重复进行模型重新训练的情况。
英文摘要
Algorithmic recourse aims to provide individuals with actionable changes to improve their predicted outcomes in high-stakes classification settings, such as loan and mortgage applications. However, most existing approaches focus only on flipping a model's prediction, without accounting for whether the recommended changes lead to genuine improvement in an individual's true qualifications or merely enable strategic gaming of the classifier. Consequently, deployed recourse policies can induce behavioral responses that degrade predictive accuracy and become ineffective after model retraining. In this work, we formalize this failure mode through a causal performative framework for recourse. We model how recourse actions propagate through a structural causal model, capturing interactions among features as well as their effect on the true label. These causal responses induce a non-convex optimization problem, even under standard convex losses. We characterize conditions under which performatively stable solutions exist and can be efficiently computed via simple iterative dynamics. Our analysis reveals that recourse policies that ignore causal structure can induce large, misaligned behavioral responses, whereas causal recourse leads to stable equilibria that reduce incentives for gaming. Experiments on both semi-synthetic and real credit datasets demonstrate that our approach consistently outperforms standard empirical risk minimization while reducing the need for repeated model retraining to accommodate distribution shifts caused by strategic agent behavior.
