We study the problem of estimation of Individual Treatment Effects (ITE) in the context of multiple treatments and networked observational data. Leveraging the network information, we aim to utilize hidden confounders that may not be directly accessible in the observed data, thereby enhancing the practical applicability of the strong ignorability assumption. To achieve this, we first employ Graph Convolutional Networks (GCN) to learn a shared representation of the confounders. Then, our approach utilizes separate neural networks to infer potential outcomes for each treatment. We design a loss function as a weighted combination of two components: representation loss and Mean Squared Error (MSE) loss on the factual outcomes. To measure the representation loss, we extend existing metrics such as Wasserstein and Maximum Mean Discrepancy (MMD) from the binary treatment setting to the multiple treatments scenario. To validate the effectiveness of our proposed methodology, we conduct a series of experiments on the benchmark datasets such as BlogCatalog and Flickr. The experimental results consistently demonstrate the superior performance of our models when compared to baseline methods.

翻译：我们研究多处理环境与网络化观测数据下个体处理效应（ITE）的估计问题。通过利用网络信息，我们旨在挖掘观测数据中可能无法直接获取的隐藏混杂因素，从而增强强可忽略性假设的实际适用性。为实现这一目标，首先采用图卷积网络（GCN）学习混杂因素的共享表征。然后，通过独立神经网络推断每种处理的潜在结果。我们设计了一个由两部分加权组合构成的损失函数：表征损失与事实结果上的均方误差（MSE）损失。为量化表征损失，我们将沃瑟斯坦距离（Wasserstein）和最大均值差异（MMD）等现有度量从二元处理设置扩展至多处理场景。为验证所提方法的有效性，我们在BlogCatalog和Flickr等基准数据集上开展系列实验。结果表明，与基准方法相比，我们的模型始终展现出更优性能。