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：保形预测是一种假设精简的方法，可为几乎任意预测模型生成无分布假设的预测区间或集合，并保证在有限样本下的覆盖概率。保形方法是统计学与机器学习领域的热门研究课题，但直至最近才被拓展至非可交换数据。本文邀请调查方法学者开始使用并推动保形方法的发展。我们介绍了如何在有限总体的基于设计推断框架下，将保形预测应用于若干常见复杂抽样调查设计的数据，并指出调查方法学者可运用其专业知识有效填补的空白领域。我们的模拟实验从经验上验证了有限样本覆盖概率的理论保证，实际数据案例则展示了如何将保形预测应用于实践中的复杂抽样调查数据。