  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.
