Graph Neural Network based Double Machine Learning Estimator of Network Causal Effects

arXiv:2403.11332v1 Announce Type: new
Abstract: 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. …

abstract arxiv causal challenge challenges confounding cs.lg cs.si data effects estimator graph graph neural network interference introduction machine machine learning neighbors network neural network paper social stat.me type units