We address the challenge of inferring causal effects in social network data. This results in challenges due to interference -- where a unit's outcome is affected by neighbors' treatments -- and network-induced confounding factors. While there is extensive literature focusing on estimating causal effects in social network setups, a majority of them make prior assumptions about the form of network-induced confounding mechanisms. Such strong assumptions are rarely likely to hold especially in high-dimensional networks. We propose a novel methodology that combines graph machine learning approaches with the double machine learning framework to enable accurate and efficient estimation of direct and peer effects using a single observational social network. We demonstrate the semiparametric efficiency of our proposed estimator under mild regularity conditions, allowing for consistent uncertainty quantification. We demonstrate that our method is accurate, robust, and scalable via an extensive simulation study. We use our method to investigate the impact of Self-Help Group participation on financial risk tolerance.

翻译：本文旨在解决社交网络数据中因果效应推断的挑战。这一挑战主要源于干扰效应——即个体结果受到邻居处理状态的影响——以及网络诱导的混杂因素。尽管已有大量文献专注于估计社交网络环境中的因果效应，但大多数方法均对网络诱导混杂机制的形式做出了先验假设。此类强假设在高维网络中尤其难以成立。我们提出了一种新颖的方法论，将图机器学习方法与双重机器学习框架相结合，从而能够利用单一观测社交网络实现直接效应与同伴效应的准确高效估计。我们在温和的正则性条件下证明了所提估计量的半参数有效性，并实现了可靠的不确定性量化。通过大量模拟研究，我们验证了该方法具有准确性、稳健性和可扩展性。最后，我们应用该方法研究了自助小组参与对金融风险承受能力的影响。