Doubly Robust Causal Estimation Under Multi-View Network Interference (Student Abstract)

Abstract Estimating causal effects under network interference is challenging especially when edges are heterogeneous and nodes share latent dependencies. We study this realistic setting and propose MVDR, a targeted maximum likelihood (TMLE) framework that learns multi-view representations of covariates and exposure on heterogeneous networks while achieving double robustness: consistency holds if either the outcome model or the exposure density is correctly specified. MVDR supports multiple network interventions using only the observed network structure. On three semi-synthetic datasets, MVDR reduces intervention-level prediction error against baselines, and remains stable under misspecification.