Causal Strategic Classification: A Tale of Two Shifts
This work addresses the challenge of training predictive models robust to strategic user behavior in classification tasks, particularly in domains like credit scoring or hiring, and is incremental as it extends the conventional framework by incorporating causal effects.
The paper tackles the problem of strategic classification by considering causal effects where user actions can change true outcomes, proposing a learning algorithm that balances between strategic behavior and causal effects to improve robustness. Experiments on synthetic and semi-synthetic data demonstrate the utility of the approach, though no concrete numbers are provided.
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.