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Interactions within complex network components define their operational modes, collective behaviors and global functionality. Understanding the role of these interactions is limited by either sensing methodologies or intentional adversarial efforts that sabotage the network structure. To overcome the partial observability and infer with good fidelity the unobserved network structures (latent subnetworks that are not random samples of the full network), we propose a general causal inferencedoi:10.1038/s41467-019-09774-x pmid:30988308 pmcid:PMC6465316 fatcat:dwgoec6hf5gjhl6yqrxvcvz5ne