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.

我们研究了多个治疗和网络观测数据背景下的个体治疗效果估计问题，利用网络信息以增强强可忽略性假设在实际应用中的适用性，并通过图卷积网络学习共享的混淆因素表示，设计了损失函数以组合表示损失和均方误差损失。通过在 BlogCatalog 和 Flickr 等基准数据集上进行一系列实验，实验结果一致表明与基准方法相比，我们的模型显示出优越的性能。

多个治疗方法网络设置中的个体因果效应估计

Estimation of individual causal effects in network setup for multiple  treatments

Due to the imbalanced nature of networked observational data, the causal
effect predictions for some individuals can severely violate the
positivity/overlap assumption, rendering unreliable estimations. Nevertheless,
this potential risk of individual-level treatment effect estimation on
networked data has been largely under-explored. To create a more trustworthy
causal effect estimator, we propose the uncertainty-aware graph deep kernel
learning (GraphDKL) framework with Lipschitz constraint to model the prediction
uncertainty with Gaussian process and identify unreliable estimations. To the
best of our knowledge, GraphDKL is the first framework to tackle the violation
of positivity assumption when performing causal effect estimation with graphs.
With extensive experiments, we demonstrate the superiority of our proposed
method in uncertainty-aware causal effect estimation on networked data.

通过不确定性感知的图深核学习（GraphDKL）框架和 Lipschitz 约束来建模预测不确定性，并识别不可靠的估计，提出了一种更加可靠的因果效应估计方法。GraphDKL 是第一个处理图形因果效应估计中正性假设违反的框架。通过广泛的实验，我们证明了在网络数据上进行不确定性感知的因果效应估计中，我们提出的方法的优越性。