Proposes a graph-regularized two-stage IV regression framework for sparse causal effect estimation and variable selection in network-structured high-dimensional data, with non-asymptotic guarantees and an application to ADNI brain imaging data.
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Network-aware IV Regression for Causal Node Discovery and Estimation
Proposes a graph-regularized two-stage IV regression framework for sparse causal effect estimation and variable selection in network-structured high-dimensional data, with non-asymptotic guarantees and an application to ADNI brain imaging data.