Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects

Estimating causal effects in social network data presents unique challenges due to the presence of spillover effects and network-induced confounding. While much of the existing literature addresses causal inference in social networks, many methods rely on strong assumptions about the form of network-induced confounding. These assumptions often fail to hold in high-dimensional networks, limiting the applicability of such approaches. To address this, we propose a novel methodology that integrates graph machine learning techniques with the double machine learning framework, facilitating accurate and efficient estimation of both direct and peer effects in a single observational social network. Our estimator achieves semiparametric efficiency under mild regularity conditions, enabling consistent uncertainty quantification. Through extensive simulations, we demonstrate the accuracy, robustness, and scalability of our method. Finally, we apply the proposed approach to examine the impact of Self-Help Group participation on financial risk tolerance, highlighting its practical relevance.