In numerous predictive scenarios, the predictive model affects the sampling distribution; for example, job applicants often meticulously craft their resumes to navigate through a screening systems. Such shifts in distribution are particularly prevalent in the realm of social computing, yet, the strategies to learn these shifts from data remain remarkably limited. Inspired by a microeconomic model that adeptly characterizes agents' behavior within labor markets, we introduce a novel approach to learn the distribution shift. Our method is predicated on a reverse causal model, wherein the predictive model instigates a distribution shift exclusively through a finite set of agents' actions. Within this framework, we employ a microfoundation model for the agents' actions and develop a statistically justified methodology to learn the distribution shift map, which we demonstrate to be effective in minimizing the performative prediction risk.

翻译：在许多预测场景中，预测模型会影响采样分布；例如，求职者通常会精心设计简历以通过筛选系统。这种分布偏移在社交计算领域尤为普遍，然而，从数据中学习这些偏移的策略仍然非常有限。受一个能够巧妙刻画劳动力市场中智能体行为的微观经济模型启发，我们提出了一种学习分布偏移的新方法。我们的方法基于一个逆向因果模型，其中预测模型仅通过有限集合的智能体行为引发分布偏移。在此框架下，我们采用微观基础模型来描述智能体行为，并开发了一种具有统计依据的方法来学习分布偏移映射。我们证明该方法能有效最小化执行预测风险。