论文标题
学习因图的因果影响
Learning Causal Effects on Hypergraphs
论文作者
论文摘要
HyperGraphs为在节点之间建模多路相互作用提供了有效的抽象，在该节点之间可以连接任何数量的节点。与大多数利用统计依赖性的研究不同，我们从因果关系的角度研究了超图。具体而言，在本文中，我们关注对超图的个人治疗效果（ITE）估计的问题，旨在估计干预措施（例如，佩戴的脸部覆盖）将对每个单个节点的结果（例如Covid-19感染）产生因果影响。关于ITE估计的现有作品假设一个人的结果不应受到其他个体的治疗作业的影响（即没有干扰），或者假设仅在普通图中的成对相互联系的个体之间存在干扰。我们认为，这些假设对现实世界中的超图可能是不现实的，在现实世界中，高阶干扰可能会影响由于存在组相互作用而导致的最终ITE估计。在这项工作中，我们研究了高阶干扰建模，并提出了一个由HyperGraph神经网络提供支持的新因果学习框架。对现实世界超图的广泛实验验证了我们框架优于现有基线的优势。
Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes. Different from most existing studies which leverage statistical dependencies, we study hypergraphs from the perspective of causality. Specifically, in this paper, we focus on the problem of individual treatment effect (ITE) estimation on hypergraphs, aiming to estimate how much an intervention (e.g., wearing face covering) would causally affect an outcome (e.g., COVID-19 infection) of each individual node. Existing works on ITE estimation either assume that the outcome on one individual should not be influenced by the treatment assignments on other individuals (i.e., no interference), or assume the interference only exists between pairs of connected individuals in an ordinary graph. We argue that these assumptions can be unrealistic on real-world hypergraphs, where higher-order interference can affect the ultimate ITE estimations due to the presence of group interactions. In this work, we investigate high-order interference modeling, and propose a new causality learning framework powered by hypergraph neural networks. Extensive experiments on real-world hypergraphs verify the superiority of our framework over existing baselines.
