Causal understanding is a fundamental goal of evidence-based medicine. When randomization is impossible, causal inference methods allow the estimation of treatment effects from retrospective analysis of observational data. However, such analyses rely on a number of assumptions, often including that of no unobserved confounding. In many practical settings, this assumption is violated when important variables are not explicitly measured in the clinical record. Prior work has proposed to address unobserved confounding with machine learning by imputing unobserved variables and then correcting for the classifier's mismeasurement. When such a classifier can be trained and the necessary assumptions are met, this method can recover an unbiased estimate of a causal effect. However, such work has been limited to synthetic data, simple classifiers, and binary variables. This paper extends this methodology by using a large language model trained on clinical notes to predict patients' smoking status, which would otherwise be an unobserved confounder. We then apply a measurement error correction on the categorical predicted smoking status to estimate the causal effect of transthoracic echocardiography on mortality in the MIMIC dataset.

翻译：因果理解是基于证据医学的基本目标。当随机化不可行时，因果推断方法允许从观察性数据的回顾性分析中估计治疗效果。然而，此类分析依赖于一系列假设，通常包括不存在未观测混杂因素的假设。在许多实际场景中，当重要变量未在临床记录中明确测量时，这一假设往往被违背。先前研究提出通过机器学习方法处理未观测混杂因素，即先对未观测变量进行插补，再校正分类器的测量误差。当此类分类器能够被训练且必要假设满足时，该方法可以恢复因果效应的无偏估计。然而，此类工作目前局限于合成数据、简单分类器和二元变量。本文通过使用基于临床记录训练的大语言模型预测患者吸烟状态（该变量在其他情况下属于未观测混杂因素），扩展了该方法论。随后，我们对分类预测的吸烟状态应用测量误差校正，以估计经胸超声心动图对MIMIC数据集中死亡率影响的因果效应。