超越权力幻觉：观测性信息系统中的准实验校准
Beyond the Illusion of Power: Calibrating Quasi-Experiments in Observational IS
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中文总结 AI 辅助
本研究通过大规模蒙特卡洛模拟，揭示观测性信息系统研究中准实验功效计算的局限性，指出序列相关、面板流失等无法被闭式公式捕捉，并强调IV识别依赖工具强度而非样本量。
中文摘要 AI 辅助
信息系统（IS）研究者越来越多地使用准实验方法，如双重差分（DiD）和工具变量（IV），从观测面板数据中恢复因果效应。为这些设计提供依据的功效计算假设误差独立同分布（i.i.d.），但更深层的问题是，即使是聚类稳健的计算器也无法看到这一点。我们报告了一项蒙特卡洛研究，覆盖9837个参数条件（约980万数据集），并分解了计划功效与实际功效之间的差距。序列相关成分可由一个考虑AR(1)的计算器在rho已知时恢复，在rho必须从短前期估计时部分恢复，但面板流失、交错采用偏差和平行趋势预检验无法由任何闭式公式捕捉；仅外生流失一项，在IS研究常用的几百到几千样本量下，就损失约8至11个百分点。与处理相关、依赖结果的流失则不仅导致功效损失，还引入偏差。对于IV，保持第一阶段F值固定，更大的N既不提高功效也不抑制排除性限制偏差，尽管在固定工具下更多数据确实会强化第一阶段，因此识别依赖于工具强度，而非样本量。
英文摘要
Information systems (IS) researchers increasingly use quasi-experimental methods such as difference-in-differences (DiD) and instrumental variables (IV) to recover causal effects from observational panel data. Power calculations that justify these designs assume i.i.d. errors, but the deeper problem is what even a cluster-robust calculator cannot see. We report a Monte Carlo study over 9837 parameter conditions (approx 9.8 million datasets) and decompose the planned-versus-achieved power gap. The serial-correlation component is recoverable by an AR(1)-aware calculator when rho is known, and partially when rho must be estimated from short pre-periods, but panel attrition, staggered-adoption bias, and parallel-trends pretesting are captured by no closed-form formula; exogenous attrition alone costs approx 8 to 11 percentage points at the few-hundred-to-thousand sample sizes IS studies use. Treatment-correlated, outcome-dependent attrition instead induces bias, not just power loss. For IV, holding first-stage F fixed, larger N neither raises power nor curbs exclusion bias, though with a fixed instrument more data does sharpen the first stage, so identification rests on instrument strength, not sample size.
发表机构
- Georgia Institute of Technology（佐治亚理工学院）
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