Replication Data for: How Much Should We Trust Instrumental Variable Estimates in Political Science? Practical Advice Based on 67 Replicated Studies

Instrumental variable (IV) strategies are widely used in political science to establish causal relationships, but the identifying assumptions required by an IV design are demanding, and assessing their validity remains challenging. In this paper, we replicate 67 articles published in three top political science journals from 2010-2022 and identify several concerning patterns. First, researchers often overestimate the strength of their instruments due to non-i.i.d. error structures such as clustering. Second, IV estimates are often highly uncertain, and the commonly used $t$-test for two-stage-least-squares (2SLS) estimates frequently underestimates the uncertainties. Third, in most replicated studies, 2SLS estimates are significantly larger than ordinary-least-squares estimates, and their ratio is inversely related to the strength of the instrument in observational studies---a pattern not observed in experimental ones---suggesting potential violations of unconfoundedness or the exclusion restriction in the former. We provide a checklist and software to help researchers avoid these pitfalls and improve their practice.工具变量（Instrumental Variable, IV）方法在政治学领域被广泛用于确立因果关系，但工具变量设计所要求的识别假设条件严苛，且对其有效性的评估仍颇具挑战。本文对2010年至2022年间发表于三本顶级政治学期刊的67篇论文开展复现研究，并从中识别出若干值得警惕的问题模式。首先，受聚类效应等非独立同分布误差结构影响，研究者往往高估自身工具变量的强度；其次，工具变量估计结果通常存在较高不确定性，而针对两阶段最小二乘（two-stage-least-squares, 2SLS）估计结果的常用t检验，时常会低估这类不确定性水平；第三，在绝大多数复现研究中，两阶段最小二乘估计结果显著大于普通最小二乘（ordinary-least-squares, OLS）估计结果，且在观测性研究中，二者的比值与工具变量强度呈负相关关系——这一模式在实验性研究中并未出现——这表明前者可能存在无混淆性（unconfoundedness）或排他性约束（exclusion restriction）被违反的情况。本文提供了一份核查清单与配套软件，以帮助研究者规避上述研究陷阱，优化实证研究实践。