Estimating causal effects from observational network data is a significant but challenging problem. Existing works in causal inference for observational network data lack an analysis of the generalization bound, which can theoretically provide support for alleviating the complex confounding bias and practically guide the design of learning objectives in a principled manner. To fill this gap, we derive a generalization bound for causal effect estimation in network scenarios by exploiting 1) the reweighting schema based on joint propensity score and 2) the representation learning schema based on Integral Probability Metric (IPM). We provide two perspectives on the generalization bound in terms of reweighting and representation learning, respectively. Motivated by the analysis of the bound, we propose a weighting regression method based on the joint propensity score augmented with representation learning. Extensive experimental studies on two real-world networks with semi-synthetic data demonstrate the effectiveness of our algorithm.

翻译：从观测网络数据中估计因果效应是一个重要但具有挑战性的问题。现有针对观测网络数据的因果推断研究缺乏对泛化界的分析，而泛化界理论上可为缓解复杂混杂偏差提供支持，并在实践中以原理性方式指导学习目标的设计。为填补这一空白，我们通过利用1）基于联合倾向得分的重加权方案和2）基于积分概率度量的表示学习方案，推导了网络场景下因果效应估计的泛化界。我们从重加权和表示学习两个角度分别给出了泛化界的分析。受界分析的启发，我们提出了一种基于联合倾向得分并辅以表示学习的加权回归方法。在两个真实网络上的半合成数据实验充分证明了我们算法的有效性。