  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.
