Estimating heterogeneous treatment effects in network settings is complicated by interference, meaning that the outcome of an instance can be influenced by the treatment status of others. Existing causal machine learning approaches usually assume a known exposure mapping that summarizes how the outcome of a given instance is influenced by others' treatment, a simplification that is often unrealistic. Furthermore, the interaction between homophily -- the tendency of similar instances to connect -- and the treatment assignment mechanism can induce a network-level covariate shift that may lead to inaccurate treatment effect estimates, a phenomenon that has not yet been explicitly studied. To address these challenges, we propose HINet, a novel method that integrates graph neural networks with domain adversarial training. This combination allows estimating treatment effects under unknown exposure mappings while mitigating the impact of (network-level) covariate shift. An extensive empirical evaluation on synthetic and semi-synthetic network datasets demonstrates the effectiveness of our approach.

翻译：在网络环境中估计异质性处理效应时，干扰效应使问题变得复杂，即个体的结果可能受到其他个体处理状态的影响。现有的因果机器学习方法通常假设存在已知的暴露映射，用以概括给定个体的结果如何受他人处理的影响，这种简化往往不符合现实。此外，同质性（相似个体相互连接的倾向）与处理分配机制之间的相互作用可能引发网络层面的协变量偏移，从而导致处理效应估计不准确，这一现象尚未得到明确研究。为解决这些挑战，我们提出了HINet方法，该方法将图神经网络与域对抗训练相结合。这种组合能够在未知暴露映射的情况下估计处理效应，同时减轻（网络层面）协变量偏移的影响。在合成与半合成网络数据集上的广泛实证评估证明了我们方法的有效性。