Conformal prediction is an assumption-lean approach to generating distribution-free prediction intervals or sets, for nearly arbitrary predictive models, with guaranteed finite-sample coverage. Conformal methods are an active research topic in statistics and machine learning, but only recently have they been extended to non-exchangeable data. In this paper, we invite survey methodologists to begin using and contributing to conformal methods. We introduce how conformal prediction can be applied to data from several common complex sample survey designs, under a framework of design-based inference for a finite population, and we point out gaps where survey methodologists could fruitfully apply their expertise. Our simulations empirically bear out the theoretical guarantees of finite-sample coverage, and our real-data example demonstrates how conformal prediction can be applied to complex sample survey data in practice.

翻译：共形预测是一种弱假设的方法，用于为几乎任意的预测模型生成无分布假设的预测区间或集合，并保证有限样本的覆盖概率。共形方法是统计学和机器学习领域的一个活跃研究课题，但直到最近才被扩展到非可交换数据。本文邀请调查方法学家开始使用并推动共形方法的发展。我们介绍了在有限总体基于设计推断的框架下，如何将共形预测应用于来自几种常见复杂样本调查设计的数据，并指出了调查方法学家可以利用其专业知识填补的研究空白。我们的模拟实验实证验证了有限样本覆盖概率的理论保证，而实际数据分析案例则展示了共形预测如何在实践中应用于复杂样本调查数据。