Selective labels occur when label observations are subject to a decision-making process; e.g., diagnoses that depend on the administration of laboratory tests. We study a clinically-inspired selective label problem called disparate censorship, where labeling biases vary across subgroups and unlabeled individuals are imputed as "negative" (i.e., no diagnostic test = no illness). Machine learning models naively trained on such labels could amplify labeling bias. Inspired by causal models of selective labels, we propose Disparate Censorship Expectation-Maximization (DCEM), an algorithm for learning in the presence of disparate censorship. We theoretically analyze how DCEM mitigates the effects of disparate censorship on model performance. We validate DCEM on synthetic data, showing that it improves bias mitigation (area between ROC curves) without sacrificing discriminative performance (AUC) compared to baselines. We achieve similar results in a sepsis classification task using clinical data.

翻译：选择性标签现象出现在标签观测受到决策过程影响时；例如，诊断结果依赖于实验室检测的执行。我们研究了一个临床启发的选择性标签问题，称为差异性审查，其中标注偏倚在不同亚组间存在差异，且未标注个体被默认为"阴性"（即未进行诊断检测等同于无疾病）。在此类标签上简单训练的机器学习模型可能会放大标注偏倚。受选择性标签因果模型的启发，我们提出了差异性审查期望最大化算法，这是一种在差异性审查存在情况下的学习算法。我们从理论上分析了DCEM如何减轻差异性审查对模型性能的影响。我们在合成数据上验证了DCEM，结果表明与基线方法相比，该算法在不牺牲判别性能的前提下改善了偏倚缓解效果。在利用临床数据进行的脓毒症分类任务中，我们取得了相似的结果。