剖析预测模型自我影响的机制，揭示其在部署中如何引发数据分布变化。
Dissecting Performative Prediction: A Comprehensive Survey
- 提出按分布映射信息完备性分类的新框架，系统梳理不同设定。
- 总结从稳定到最优的双重优化目标，明确各类方法的适用场景。
- 揭示与因果推理、在线学习等领域的深层关联，启发新研究方向。
performative prediction 自2020年奠基以来，关注预测模型部署后导致环境分布变化，造成模型预期分布与真实分布不匹配的问题。这种分布偏移由称为分布映射（distribution map）的函数定义。五年间，该领域发展出多种解决方案、理论分析及扩展设定，并与因果推断、在线学习等方向产生交叉。本文首先阐明performative prediction的基本设定，区分性能稳定性和性能最优性两种优化目标；提出基于分布映射信息可得性的新分类体系；综述现有分布映射实现方式与应对策略；最后揭示其与多个成熟领域的已知及潜在关联，旨在推动未来研究。
原文摘要 · Abstract (English)
The field of performative prediction had its beginnings in 2020 with the seminal paper "Performative Prediction" by Perdomo et al., which established a novel machine learning setup where the deployment of a predictive model causes a distribution shift in the environment, which in turn causes a mismatch between the distribution expected by the predictive model and the real distribution. This shift is defined by a so-called distribution map. In the half-decade since, a literature has emerged which has, among other things, introduced new solution concepts to the original setup, extended the setup, offered new theoretical analyses, and examined the intersection of performative prediction and other established fields. In this survey, we first lay out the performative prediction setting and explain the different optimization targets: performative stability and performative optimality. We introduce a new way of classifying different performative prediction settings, based on how much information is available about the distribution map. We survey existing implementations of distribution maps and existing methods to address the problem of performative prediction, while examining different ways to categorize them. Finally, we point out known and previously unknown connections that can be drawn to other fields, in the hopes of stimulating future research.
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