我们应在多大程度上信任政治学中的工具变量估计？基于60余项复制研究的实用建议
How Much Should We Trust Instrumental Variable Estimates in Political Science? Practical Advice Based on Over 60 Replicated Studies
AI总结：
本研究复制67篇政治学顶刊论文，发现IV强度常被高估、2SLS的t检验低估不确定性且部分估计功效不足、非实验IV的2SLS与OLS估计比值和IV强度负相关等问题，并给出实践检查清单。
摘要：
工具变量（Instrumental Variable，IV）策略在政治学中被广泛用于建立因果关系。然而，IV设计所需的识别假设要求极高，研究人员仍难以评估其有效性。本文复制了2010-2022年间发表在三大政治学顶级期刊上的67篇论文，发现了几个令人担忧的模式。首先，由于存在聚类结构等非独立同分布（non-i.i.d.）误差，研究人员往往高估了其IV的强度。其次，最常用于两阶段最小二乘（two-stage-least-squares，2SLS）估计的t检验通常会严重低估不确定性。通过使用更稳健的推断方法，我们发现样本中约19%-30%的2SLS估计量统计功效不足。第三，在大多数复制研究中，2SLS估计值远大于普通最小二乘估计值，且在IV非实验生成的研究中，二者的比值与IV强度呈负相关，这表明可能存在无混淆性假设或排他性约束被违反的情况。为帮助研究人员规避这些陷阱，我们提供了一份优化实践的检查清单。
英文摘要：
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.
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