We consider the problem of estimating the average treatment effect (ATE) in a semi-supervised learning setting, where a very small proportion of the entire set of observations are labeled with the true outcome but features predictive of the outcome are available among all observations. This problem arises, for example, when estimating treatment effects in electronic health records (EHR) data because gold-standard outcomes are often not directly observable from the records but are observed for a limited number of patients through small-scale manual chart review. We develop an imputation-based approach for estimating the ATE that is robust to misspecification of the imputation model. This effectively allows information from the predictive features to be safely leveraged to improve efficiency in estimating the ATE. The estimator is additionally doubly-robust in that it is consistent under correct specification of either an initial propensity score model or a baseline outcome model. It is also locally semiparametric efficient under an ideal semi-supervised model where the distribution of the unlabeled data is known. Simulations exhibit the efficiency and robustness of the proposed method compared to existing approaches in finite samples.We illustrate the method by comparing rates of treatment response to two biologic agents for treatment inflammatory bowel disease using EHR data from Partner's Healthcare. This article is protected by copyright. All rights reserved.

中文翻译：

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应用于电子健康记录数据的平均治疗效果的稳健且高效的半监督估计


我们考虑在半监督学习环境中估计平均治疗效果（ATE）的问题，其中整组观察中的一小部分被标记为真实结果，但所有观察中都可以使用预测结果的特征。例如，在估计电子健康记录 (EHR) 数据中的治疗效果时，就会出现这个问题，因为金标准结果通常无法直接从记录中观察到，而是通过小规模手动图表审查对有限数量的患者进行观察。我们开发了一种基于插补的方法来估计 ATE，该方法对于插补模型的错误指定具有鲁棒性。这有效地允许安全地利用来自预测特征的信息来提高估计 ATE 的效率。该估计器还具有双重鲁棒性，因为它在初始倾向评分模型或基线结果模型的正确规范下是一致的。在已知未标记数据分布的理想半监督模型下，它也是局部半参数有效的。与有限样本中的现有方法相比，模拟显示了所提出的方法的效率和稳健性。我们通过使用合作伙伴医疗保健的 EHR 数据比较两种治疗炎症性肠病的生物制剂的治疗反应率来说明该方法。本文受版权保护。版权所有。