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.

中文翻译：

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GraphITE：估计图结构处理的个体效应

对目标个体的治疗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处理二元或多项选择处理；然而，在某些应用中，治疗次数可能非常多，而治疗本身具有丰富的信息。在这项研究中，我们考虑了此类案例的一个重要实例：药物等图结构治疗的结果估计问题。由于存在大量可能的治疗方法，常规治疗效果估计中出现的观察数据的反事实性质变得更受关注。我们提出的方法，GraphITE（发音为“graphite” ) 使用图神经网络学习图结构治疗的表示，同时使用 Hilbert-Schmidt 独立准则正则化来减轻观察偏差，这增加了目标和治疗表示的独立性。在两个真实世界数据集上的实验表明，GraphITE 优于基线，尤其是在有大量处理的情况下。