When users can benefit from certain predictive outcomes, they may be prone to act to achieve those outcome, e.g., by strategically modifying their features. The goal in strategic classification is therefore to train predictive models that are robust to such behavior. However, the conventional framework assumes that changing features does not change actual outcomes, which depicts users as "gaming" the system. Here we remove this assumption, and study learning in a causal strategic setting where true outcomes do change. Focusing on accuracy as our primary objective, we show how strategic behavior and causal effects underlie two complementing forms of distribution shift. We characterize these shifts, and propose a learning algorithm that balances between these two forces and over time, and permits end-to-end training. Experiments on synthetic and semi-synthetic data demonstrate the utility of our approach.

翻译：当用户能从某些预测结果中获益时，他们可能倾向于采取行动以获取这些结果，例如通过策略性地修改自身特征。因此，策略分类的目标是训练出对此类行为具有鲁棒性的预测模型。然而，传统框架假设改变特征并不会改变实际结果，这描绘了用户“玩弄”系统的情形。本文去除了这一假设，并研究真实结果确实发生变化的因果策略环境中的学习问题。我们以准确性为主要目标，展示了策略行为和因果效应如何构成两种互补形式的分布偏移。我们刻画了这些偏移，并提出了一种学习算法，该算法能够在这两种力之间取得平衡，并随时间推移支持端到端训练。在合成数据和半合成数据上的实验证明了我们方法的有效性。