论文标题
石墨：估计图形结构处理的个人效应
GraphITE: Estimating Individual Effects of Graph-structured Treatments
论文作者
论文摘要
目标个体治疗的结果估计是基于因果关系的决策的重要基础。大多数现有的结果估计方法涉及二进制或多项选择治疗；但是，在某些应用中，治疗的数量可能很大，而治疗本身具有丰富的信息。在这项研究中，我们考虑了这种情况的一个重要实例：图形结构治疗（例如药物）的结果估计问题。由于大量可能的治疗方法，在常规治疗效应估计中出现的观察数据的反事实性质更加成为该问题的关注点。我们提出的方法，石墨（发音为“石墨”）使用图神经网络了解图形结构处理的表示，同时使用Hilbert-Schmidt独立标准正则化来减轻观察偏见，从而增加了目标和处理的表示。两个现实世界数据集的实验表明，石墨优于基准，尤其是在有大量治疗的情况下。
Outcome estimation of treatments for target individuals is an important foundation for decision making based on causal relations. Most existing outcome estimation methods deal with binary or multiple-choice treatments; however, in some applications, the number of treatments can be significantly large, while the treatments themselves have rich information. In this study, we considered one important instance of such cases: the outcome estimation problem of graph-structured treatments such as drugs. Owing to the large number of possible treatments, the counterfactual nature of observational data that appears in conventional treatment effect estimation becomes more of a concern for this problem. Our proposed method, GraphITE (pronounced "graphite") learns the representations of graph-structured treatments using graph neural networks while mitigating observation biases using Hilbert-Schmidt Independence Criterion regularization, which increases the independence of the representations of the targets and treatments. Experiments on two real-world datasets show that GraphITE outperforms baselines, especially in cases with a large number of treatments.
