Research
NEDNet: Decomposing Network Effects for Causal Estimation under Interference
Researchers propose NEDNet, a neural network that decomposes outcome responses into baseline, treatment, and exposure pathways for causal effect estimation under network interference. It uses path-specific neighborhood contextualization and a reference-normalized decomposition to improve accuracy. Experiments on BlogCatalog and Flickr benchmarks show NEDNet achieves the lowest PEHE for individual, peer, and total effects.
causal effect estimationnetwork datatreatment effects