论文标题
半监督个体治疗效应估计的反事实传播
Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation
论文作者
论文摘要
个体治疗效果（ITE）代表对特定行动采取特定行动的预期改善，并且在各个领域的决策中起着重要作用。但是，其估计问题很困难，因为干预研究以收集有关应用治疗的信息（即行动），其结果通常在时间和货币成本方面非常昂贵。在这项研究中，我们考虑了一个半监督的ITE估计问题，该问题利用了更容易获得的未标记实例，以使用小标记的数据来提高ITE估计的性能。我们结合了因果推理和半监督学习的两个思想，分别是匹配和标签传播，以提出反事实传播，这是第一种半监督的ITE估计方法。使用半销售数据集的实验表明，所提出的方法可以成功缓解ITE估计中的数据稀缺问题。
Individual treatment effect (ITE) represents the expected improvement in the outcome of taking a particular action to a particular target, and plays important roles in decision making in various domains. However, its estimation problem is difficult because intervention studies to collect information regarding the applied treatments (i.e., actions) and their outcomes are often quite expensive in terms of time and monetary costs. In this study, we consider a semi-supervised ITE estimation problem that exploits more easily-available unlabeled instances to improve the performance of ITE estimation using small labeled data. We combine two ideas from causal inference and semi-supervised learning, namely, matching and label propagation, respectively, to propose counterfactual propagation, which is the first semi-supervised ITE estimation method. Experiments using semi-real datasets demonstrate that the proposed method can successfully mitigate the data scarcity problem in ITE estimation.
