In settings where Machine Learning (ML) algorithms automate or inform
consequential decisions about people, individual decision subjects are often
incentivized to strategically modify their observable attributes to receive
more favorable predictions. As a result, the distribution the assessment rule
is trained on may differ from the one it operates on in deployment. While such
distribution shifts, in general, can hinder accurate predictions, our work
identifies a unique opportunity associated with shifts due to strategic
responses: We show that we can use strategic responses effectively to recover
causal relationships between the observable features and outcomes we wish to
predict, even under the presence of unobserved confounding variables.
Specifically, our work establishes a novel connection between strategic
responses to ML models and instrumental variable (IV) regression by observing
that the sequence of deployed models can be viewed as an instrument that
affects agents' observable features but does not directly influence their
outcomes. We show that our causal recovery method can be utilized to improve
decision-making across several important criteria: individual fairness, agent
outcomes, and predictive risk. In particular, we show that if decision subjects
differ in their ability to modify non-causal attributes, any decision rule
deviating from the causal coefficients can lead to (potentially unbounded)
individual-level unfairness.

研究发现，当机器学习算法自动或参考作用于关于人的重大决策时，个体决策受到激励可通过策略性地修改自己的可观察属性以获得更有利的预测，导致预测所依赖的训练数据分布与实际使用的数据分布不同。但是，该文提出了一个新方法，通过观察部署模型的顺序可以将策略性响应与工具变量回归联系起来，从而有效地恢复我们想要预测的可观察特征和结果之间的因果关系，从而提高决策的公平性、代理结果和预测风险。