Instrumental variable (IV) strategies are widely used in political science to establish causal relationships. However, the identifying assumptions required by an IV design are demanding, and it remains challenging for researchers to assess their validity. In this paper, we replicate 67 papers published in three top journals in political science during 2010-2022 and identify several troubling patterns. First, researchers often overestimate the strength of their IVs due to non-i.i.d. errors, such as a clustering structure. Second, the most commonly used t-test for the two-stage-least-squares (2SLS) estimates often severely underestimates uncertainty. Using more robust inferential methods, we find that around 19-30% of the 2SLS estimates in our sample are underpowered. Third, in the majority of the replicated studies, the 2SLS estimates are much larger than the ordinary-least-squares estimates, and their ratio is negatively correlated with the strength of the IVs in studies where the IVs are not experimentally generated, suggesting potential violations of unconfoundedness or the exclusion restriction. To help researchers avoid these pitfalls, we provide a checklist for better practice.

翻译：工具变量（IV）策略广泛应用于政治学中以建立因果关系。然而，IV设计所需的识别假设要求严苛，研究者评估其有效性仍面临挑战。本文对2010-2022年间发表于政治学三大顶级期刊的67篇论文进行重复研究，发现了若干令人担忧的规律。第一，由于非独立同分布误差（如聚类结构），研究者常高估其工具变量的强度。第二，最常用的两阶段最小二乘法（2SLS）估计的t检验往往严重低估不确定性。采用更稳健的推断方法后，我们发现样本中约19%-30%的2SLS估计统计功效不足。第三，在多数重复研究中，2SLS估计值远大于普通最小二乘法估计值，且当工具变量非实验生成时，二者比值与工具变量强度呈负相关，这暗示可能存在无混杂性假设或排他性约束的违背。为帮助研究者规避这些陷阱，我们提供了优化实践的操作清单。