A growing number of empirical models exhibit set-valued predictions. This paper develops a tractable inference method with finite-sample validity for such models. The proposed procedure uses a robust version of the universal inference framework by Wasserman et al. (2020) and avoids using moment selection tuning parameters, resampling, or simulations. The method is designed for constructing confidence intervals for counterfactual objects and other functionals of the underlying parameter. It can be used in applications that involve model incompleteness, discrete and continuous covariates, and parameters containing nuisance components.

翻译：越来越多的实证模型展现出集合值预测特性。本文针对此类模型提出了一种具有有限样本有效性的可处理推断方法。所提出的程序采用Wasserman等人（2020）通用推断框架的稳健版本，避免了矩选择调参、重抽样或模拟运算。该方法专为构建反事实对象及基础参数其他泛函的置信区间而设计，适用于包含模型不完全性、离散与连续协变量以及含冗余成分参数的应用场景。