预测模型会改变人群行为，该研究提出方法量化长期社会影响。
Microfoundation Inference for Strategic Prediction
- 将用户响应建模为成本调整的效用最大化问题
- 用最优传输对齐模型前后的分布，估计长期影响
- 在信贷评分数据上验证了方法的有效性
在许多预测任务中，预测模型本身会影响目标变量的分布，这种现象称为绩效预测。这种影响通常源于利益相关者为应对预测结果而采取的战略行动。当前机器学习领域广泛采用绩效预测的主要障碍在于从业者普遍缺乏对其预测的社会影响的认知。为此，本文提出一种学习分布映射的方法，以刻画预测模型对人群的长期影响。具体而言，将代理者的反应建模为成本调整的效用最大化问题，并提出了相应的成本估计方法。该方法利用最优传输技术，对模型暴露前（ex ante）和暴露后（ex post）的分布进行对齐。我们给出了所提估计量的收敛速率，并通过在信贷评分数据集上的实证分析评估了其性能。
原文摘要 · Abstract (English)
Often in prediction tasks, the predictive model itself can influence the distribution of the target variable, a phenomenon termed performative prediction. Generally, this influence stems from strategic actions taken by stakeholders with a vested interest in predictive models. A key challenge that hinders the widespread adaptation of performative prediction in machine learning is that practitioners are generally unaware of the social impacts of their predictions. To address this gap, we propose a methodology for learning the distribution map that encapsulates the long-term impacts of predictive models on the population. Specifically, we model agents' responses as a cost-adjusted utility maximization problem and propose estimates for said cost. Our approach leverages optimal transport to align pre-model exposure (ex ante) and post-model exposure (ex post) distributions. We provide a rate of convergence for this proposed estimate and assess its quality through empirical demonstrations on a credit-scoring dataset.
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