Identifying and measuring biases associated with sensitive attributes is a crucial consideration in healthcare to prevent treatment disparities. One prominent issue is inaccurate pulse oximeter readings, which tend to overestimate oxygen saturation for dark-skinned patients and misrepresent supplemental oxygen needs. Most existing research has revealed statistical disparities linking device measurement errors to patient outcomes in intensive care units (ICUs) without causal formalization. This study causally investigates how racial discrepancies in oximetry measurements affect invasive ventilation in ICU settings. We employ a causal inference-based approach using path-specific effects to isolate the impact of bias by race on clinical decision-making. To estimate these effects, we leverage a doubly robust estimator, propose its self-normalized variant for improved sample efficiency, and provide novel finite-sample guarantees. Our methodology is validated on semi-synthetic data and applied to two large real-world health datasets: MIMIC-IV and eICU. Contrary to prior work, our analysis reveals minimal impact of racial discrepancies on invasive ventilation rates. However, path-specific effects mediated by oxygen saturation disparity are more pronounced on ventilation duration, and the severity differs across datasets. Our work provides a novel pipeline for investigating potential disparities in clinical decision-making and, more importantly, highlights the necessity of causal methods to robustly assess fairness in healthcare.

翻译：识别和测量与敏感属性相关的偏差是医疗保健领域防止治疗差异的关键考量。一个突出问题是不准确的脉搏血氧仪读数，其倾向于高估深肤色患者的血氧饱和度，并错误反映补充氧气的需求。现有研究大多揭示了重症监护室（ICU）中设备测量误差与患者预后之间存在统计关联的差异，但缺乏因果形式化。本研究从因果角度探讨了血氧测量中的种族差异如何影响ICU环境下的有创通气决策。我们采用基于因果推断的方法，利用路径特异性效应来分离种族偏差对临床决策的影响。为估计这些效应，我们采用了双重稳健估计器，提出了其自归一化变体以提高样本效率，并提供了新颖的有限样本保证。我们的方法在半合成数据上得到验证，并应用于两个大型真实世界健康数据集：MIMIC-IV和eICU。与先前研究相反，我们的分析揭示了种族差异对有创通气率的影响微乎其微。然而，由血氧饱和度差异介导的路径特异性效应对通气持续时间的影响更为显著，且其严重程度在不同数据集间存在差异。我们的工作为探究临床决策中潜在差异提供了一条新颖的分析路径，更重要的是，强调了采用因果方法以稳健评估医疗保健公平性的必要性。