Graph Neural Network based Double Machine Learning Estimator of Network
  Causal Effects
Seyedeh Baharan Khatami Harsh Parikh Haowei Chen Sudeepa Roy Babak Salimi
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.