Our paper addresses the challenge of inferring causal effects in social network data, characterized by complex interdependencies among individuals resulting in challenges such as non-independence of units, interference (where a unit's outcome is affected by neighbors' treatments), and introduction of additional confounding factors from neighboring units. We propose a novel methodology combining graph neural networks and double machine learning, enabling accurate and efficient estimation of direct and peer effects using a single observational social network. Our approach utilizes graph isomorphism networks in conjunction with double machine learning to effectively adjust for network confounders and consistently estimate the desired causal effects. We demonstrate that our estimator is both asymptotically normal and semiparametrically efficient. A comprehensive evaluation against four state-of-the-art baseline methods using three semi-synthetic social network datasets reveals our method's on-par or superior efficacy in precise causal effect estimation. Further, we illustrate the practical application of our method through a case study that investigates the impact of Self-Help Group participation on financial risk tolerance. The results indicate a significant positive direct effect, underscoring the potential of our approach in social network analysis. Additionally, we explore the effects of network sparsity on estimation performance.

翻译：本文针对社交网络数据中因果效应推断的挑战展开研究，这类数据具有个体间复杂相互依赖性，导致单元非独立性、干扰效应（个体结果受邻居处理影响）以及来自邻居单元的额外混杂因素引入等问题。我们提出一种融合图神经网络与双重机器学习的创新方法，可利用单次观测的社交网络数据准确高效地估计直接效应与同伴效应。该方法通过结合图同构网络与双重机器学习，有效调整网络混杂因素，实现目标因果效应的一致估计。我们证明所提估计量具有渐近正态性和半参数有效性。在三个半合成社交网络数据集上，与四种前沿基线方法的综合对比表明，本方法在精确因果效应估计方面达到或超越现有水平。进一步通过自助小组参与对金融风险承受能力影响的案例研究，展示了方法的应用价值：结果揭示显著的正向直接效应，凸显该方法在社交网络分析中的潜力。此外，我们还探讨了网络稀疏性对估计性能的影响。