The estimation of racial disparities in health care, financial services, voting, and other contexts is often hampered by the lack of individual-level racial information in administrative records. In many cases, the law prohibits the collection of such information to prevent direct racial discrimination. As a result, many analysts have adopted Bayesian Improved Surname Geocoding (BISG), which combines individual names and addresses with the Census data to predict race. Although BISG tends to produce well-calibrated racial predictions, its residuals are often correlated with the outcomes of interest, yielding biased estimates of racial disparities. We propose an alternative identification strategy that corrects this bias. The proposed strategy is applicable whenever one's surname is conditionally independent of the outcome given their (unobserved) race, residence location, and other observed characteristics. Leveraging this identification strategy, we introduce a new class of models, Bayesian Instrumental Regression for Disparity Estimation (BIRDiE), that estimate racial disparities by using surnames as a high-dimensional instrumental variable for race. Our estimation method is scalable, making it possible to analyze large-scale administrative data. We also show how to address potential violations of the key identification assumptions. A validation study based on the North Carolina voter file shows that BIRDiE reduces error by up to 84% in comparison to the standard approaches for estimating racial differences in party registration. Open-source software is available which implements the proposed methodology.

翻译：在医疗保健、金融服务、投票及其他领域中，对种族差异的评估常因行政记录中缺乏个体层面的种族信息而受阻。在许多情况下，法律禁止收集此类信息以防止直接的种族歧视。因此，许多分析者采用贝叶斯改进型姓氏地理编码法（BISG），该方法将个体姓名和地址与人口普查数据相结合以预测种族。尽管BISG往往能产生校准良好的种族预测，但其残差常与关注的结果变量相关，导致种族差异评估产生有偏估计。我们提出了一种替代性识别策略来修正这一偏差。当个体的姓氏在给定其（未观测的）种族、居住地点及其他观测特征后条件独立于结果变量时，该策略即可适用。基于这一识别策略，我们引入了一类新模型——用于差异评估的贝叶斯工具变量回归模型（BIRDiE），该模型通过将姓名作为种族的高维工具变量来评估种族差异。我们的估计方法具有可扩展性，能够分析大规模行政数据。同时，我们还展示了如何处理关键识别假设可能被违反的情况。基于北卡罗来纳州选民档案的验证研究表明，在评估政党登记的种族差异时，BIRDiE相比标准方法最多可减少84%的误差。我们提供了实现该方法的开源软件。