Departments of Health Services, Pharmacy and Economics, University of Washington, Seattle, WA, USA; The National Bureau of Economic Research, Cambridge, MA, USA.
Health Econ. 2014 Apr;23(4):462-72. doi: 10.1002/hec.2926. Epub 2013 Jun 13.
In the outcomes research and comparative effectiveness research literature, there are strong cautionary tales on the use of instrumental variables (IVs) that may influence the newly initiated to shun this premier tool for casual inference without properly weighing their advantages. It has been recommended that IV methods should be avoided if the instrument is not econometrically perfect. The fact that IVs can produce better results than naïve regression, even in nonideal circumstances, remains underappreciated. In this paper, we propose a diagnostic criterion and related software that can be used by an applied researcher to determine the plausible superiority of IV over an ordinary least squares (OLS) estimator, which does not address the endogeneity of a covariate in question. Given a reasonable lower bound for the bias arising out of an OLS estimator, the researcher can use our proposed diagnostic tool to confirm whether the IV at hand can produce a better estimate (i.e., with lower mean square error) of the true effect parameter than the OLS, without knowing the true level of contamination in the IV.
在结局研究和比较效果研究文献中，有很多关于工具变量（IV）使用的警示故事，这可能会导致新学者回避这种用于因果推断的主要工具，而不恰当地权衡其优势。有人建议，如果工具变量在计量经济学上并不完美，就应该避免使用 IV 方法。IV 即使在不理想的情况下也能产生比简单回归更好的结果，这一点仍未得到充分认识。在本文中，我们提出了一种诊断标准和相关软件，应用研究人员可以使用这些标准和软件来确定 IV 是否优于普通最小二乘法（OLS）估计量，而后者不能解决所讨论的协变量的内生性问题。给定 OLS 估计量产生偏差的合理下限，研究人员可以使用我们提出的诊断工具来确认手头的 IV 是否可以比 OLS 产生更好的真实效应参数估计（即均方误差更低），而无需了解 IV 的真实污染水平。
