Estimating the individual treatment effect (ITE) from observational data is a crucial research topic that holds significant value across multiple domains. How to identify hidden confounders poses a key challenge in ITE estimation. Recent studies have incorporated the structural information of social networks to tackle this challenge, achieving notable advancements. However, these methods utilize graph neural networks to learn the representation of hidden confounders in Euclidean space, disregarding two critical issues: (1) the social networks often exhibit a scalefree structure, while Euclidean embeddings suffer from high distortion when used to embed such graphs, and (2) each ego-centric network within a social network manifests a treatment-related characteristic, implying significant patterns of hidden confounders. To address these issues, we propose a novel method called Treatment-Aware Hyperbolic Representation Learning (TAHyper). Firstly, TAHyper employs the hyperbolic space to encode the social networks, thereby effectively reducing the distortion of confounder representation caused by Euclidean embeddings. Secondly, we design a treatment-aware relationship identification module that enhances the representation of hidden confounders by identifying whether an individual and her neighbors receive the same treatment. Extensive experiments on two benchmark datasets are conducted to demonstrate the superiority of our method.

翻译：从观测数据中估计个体治疗效果（ITE）是一个具有多领域重要价值的关键研究课题。如何识别隐藏混杂变量是ITE估计的核心挑战。近年来，研究者利用社交网络的结构信息应对这一挑战并取得显著进展。然而，现有方法在欧几里得空间中通过图神经网络学习隐藏混杂变量的表示，忽视了两个关键问题：（1）社交网络通常呈现无标度结构，而欧几里得嵌入方法在嵌入此类图时存在高度失真；（2）社交网络中的每个自我中心网络均表现出与治疗相关的特征，这暗示了隐藏混杂变量的重要模式。为解决上述问题，我们提出了一种名为"治疗意识双曲表示学习"（TAHyper）的新方法。首先，TAHyper采用双曲空间编码社交网络，有效降低欧几里得嵌入导致的混杂变量表示失真。其次，我们设计了一个治疗意识关系识别模块，通过判断个体及其邻居是否接受相同治疗，增强隐藏混杂变量的表示能力。在两个基准数据集上的大量实验证明了该方法的优越性。