To examine the robustness of their results against omitted variable bias, management researchers often compare the Impact Threshold of a Confounding Variable (ITCV) with control variable correlations. This paper describes three issues with this approach. First, the ITCV and control variable correlations are measured on mathematically different scales. As a result, their direct comparison is inappropriate. Second, a fair comparison requires a rescaled version of the ITCV known as “the unconditional ITCV.” Third, even the interpretation of the unconditional ITCV is complicated by the presence of multiple omitted variables, numerous control variables, and correlations between the omitted and control variables. We illustrate these issues with simple computer-generated data, a Monte Carlo simulation, and a practical application based on a published dataset. These results suggest that rules of thumb based on ITCV and control variable correlations are misleading and call for alternative ways of running, interpreting, and reporting the ITCV.

中文翻译：

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Hic Sunt Dracones：关于 ITCV 与控制变量相关性比较的风险


为了检查他们的结果对遗漏的变量偏差的稳健性，管理研究人员经常将混杂变量的影响阈值 （ITCV） 与控制变量相关性进行比较。本文介绍了这种方法的三个问题。首先，ITCV 和控制变量相关性在数学上不同的尺度上进行测量。因此，它们的直接比较是不合适的。其次，公平的比较需要一个重新调整的 ITCV 版本，称为“无条件 ITCV”。第三，由于存在多个省略的变量、众多的控制变量以及省略变量和控制变量之间的相关性，即使是对无条件 ITCV 的解释也很复杂。我们通过简单的计算机生成数据、蒙特卡洛模拟和基于已发布数据集的实际应用程序来说明这些问题。这些结果表明，基于 ITCV 和控制变量相关性的经验法则具有误导性，并需要运行、解释和报告 ITCV 的替代方法。