Instrumental variable methods are among the most commonly used causal inference approaches to deal with unmeasured confounders in observational studies. The presence of invalid instruments is the primary concern for practical applications, and a fast-growing area of research is inference for the causal effect with possibly invalid instruments. This paper illustrates that the existing confidence intervals may undercover when the valid and invalid instruments are hard to separate in a data-dependent way. To address this, we construct uniformly valid confidence intervals that are robust to the mistakes in separating valid and invalid instruments. We propose to search for a range of treatment effect values that lead to sufficiently many valid instruments. We further devise a novel sampling method, which, together with searching, leads to a more precise confidence interval. Our proposed searching and sampling confidence intervals are uniformly valid and achieve the parametric length under the finite-sample majority and plurality rules. We apply our proposal to examine the effect of education on earnings. The proposed method is implemented in the R package RobustIV available from CRAN.

翻译：工具变量方法是观察性研究中处理未测量混杂因素最常用的因果推断方法之一。无效工具变量的存在是实际应用中的主要关注点，而针对可能含无效工具变量的因果效应推断已成为一个快速发展的研究领域。本文指出，当有效与无效工具变量难以通过数据驱动方式明确区分时，现有置信区间可能存在覆盖不足的问题。为解决这一问题，我们构建了在区分有效与无效工具变量时对错误具有鲁棒性的均匀有效置信区间。我们提出通过搜索能产生足够多有效工具变量的处理效应值范围，并进一步设计了一种新颖的采样方法，该方法与搜索相结合可得到更精确的置信区间。我们所提出的搜索与采样置信区间满足均匀有效性，并在有限样本多数规则和主导规则下达到参数长度。我们将该方法应用于检验教育对收入的影响，并通过CRAN平台上的R包RobustIV实现。