Estimating causal effects from high-dimensional, structured exposures is a fundamental challenge in modern applications ranging from neuroscience and finance to environmental science. While the literature has addressed high-dimensional instrumental variable (IV) regression, and separately leveraged graph structure in penalized regression, the integration of both, especially for causal support recovery in the presence of latent confounding, remains unexplored. In this work, we propose a novel two-stage regression framework that incorporates instrumental variables and graph-based regularization to uncover sparse causal effects among network-structured exposures. Our method accommodates both valid and partially invalid instruments, and encourages structural similarity among connected predictors through a graph-fused penalty. We establish non-asymptotic guarantees for estimation accuracy and causal variable selection, and demonstrate that our approach yields improved performance over existing methods that ignore network dependencies or invalid IVs. Applied to ADNI brain imaging and genetic data, our method identifies interpretable causal ROIs associated with cognitive outcomes, underscoring the utility of graph-assisted IV regression in neuroscience and beyond.

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