We propose a method for constructing distribution-free prediction intervals in nonparametric instrumental variable regression (NPIV), with finite-sample coverage guarantees. Building on the conditional guarantee framework in conformal inference, we reformulate conditional coverage as marginal coverage over a class of IV shifts $\mathcal{F}$. Our method can be combined with any NPIV estimator, including sieve 2SLS and other machine-learning-based NPIV methods such as neural networks minimax approaches. Our theoretical analysis establishes distribution-free, finite-sample coverage over a practitioner-chosen class of IV shifts.

翻译：我们提出了一种方法，用于在非参数工具变量回归（NPIV）中构建无分布假设的预测区间，并保证有限样本覆盖。基于保形推断中的条件保证框架，我们将条件覆盖重新表述为对工具变量位移类$\mathcal{F}$的边际覆盖。该方法可与任意NPIV估计量结合使用，包括筛分两阶段最小二乘法以及其他基于机器学习的NPIV方法（如神经网络极小极大方法）。我们的理论分析为从业者选择的工具变量位移类建立了无分布假设的有限样本覆盖保证。