In strategic classification, an institution (e.g., a bank) anticipates adaptation from users who change their features to increase utility in a classification task (e.g., loan repayment). Since a key challenge is the distribution shift induced by users, we turn to causal models, which have been shown to bound the worst-case out-of-distribution (OOD) risk, and establish several new results that link causality and strategic classification. First, we show that causal classification leads to optimal classification error after any sufficiently large adaptation, when the noise is bounded in a certain way. Second, when these assumptions do not hold, we show OOD cross-entropy risk of optimal classifiers decomposes into an OOD bias term and a term arising from not using all observable features, allowing us to understand when causal classifiers have an advantage. Finally, we show that the use of causal features can allow alignment of long-term incentives between institutions and users, contrasting with previous work that highlights social costs of such approaches. We validate our theory empirically on synthetic data, finding that our results predict behavior in practice.

翻译：在策略分类中，机构（如银行）需预判用户为提升分类任务（如贷款偿还）效用而改变其特征的适应性行为。针对用户行为引发的分布偏移这一核心挑战，我们转向因果模型——该模型已被证明可限制最坏情况下的分布外风险，并建立了连接因果性与策略分类的若干新结论。首先，我们证明：当噪声以特定方式有界时，经过充分大的适应性调整后，因果分类可实现最优分类误差。其次，若这些假设不成立，我们证明最优分类器的分布外交叉熵风险可分解为分布外偏差项与未使用所有可观测特征所导致的误差项，从而揭示因果分类器何时具有优势。最后，我们证明使用因果特征可促使机构与用户长期激励对齐，这与先前工作强调此类方法具有社会成本的观点形成对比。我们通过合成数据实验验证了理论，发现结论能有效预测实际行为。