Omitted variables are one of the most important threats to the identification of causal effects. Several widely used methods assess the impact of omitted variables on empirical conclusions by comparing measures of selection on observables with measures of selection on unobservables. The recent literature has discussed various limitations of these existing methods, however. This includes challenges that arise when the omitted variables are endogenous, meaning that they are correlated with the included controls. We develop a new approach to regression sensitivity analysis that avoids those limitations, while still allowing researchers to calibrate sensitivity parameters by comparing the magnitude of selection on observables with the magnitude of selection on unobservables as in previous methods. We illustrate our results in an empirical study of the effect of historical American frontier life on modern cultural beliefs. Finally, we implement these methods in the companion Stata module regsensitivity for easy use in practice.

翻译：遗漏变量是识别因果效应的最重要威胁之一。若干广泛使用的方法通过比较可观测变量的选择程度与不可观测变量的选择程度，来评估遗漏变量对实证结论的影响。然而，近期文献讨论了这些现有方法的多种局限性。这包括当遗漏变量具有内生性（即与已纳入的控制变量相关）时所产生的挑战。我们提出了一种新的回归敏感性分析方法，该方法避免了这些局限性，同时仍允许研究者像先前方法那样，通过比较可观测变量与不可观测变量的选择程度来校准敏感性参数。我们在关于美国历史边疆生活对现代文化信念影响的实证研究中展示了我们的结果。最后，我们在配套的Stata模块regsensitivity中实现了这些方法，以便于实际应用。