arXiv:2611.07879v1 Announce Type: cross
Abstract: Estimating causal effects from observational network data requires accounting for both individual treatment and neighborhood exposure. Existing outcome models commonly incorporate these intervention variables into a joint predictor and estimate causal effects through prediction contrasts, leaving the corresponding response components implicit. We propose the $\underline{N}$etwork $\underline{E}$ffect $\underline{D}$ecomposition $\underline{Net}$work (NEDNet), which parameterizes the conditional outcome response as a query-invariant pre-treatment baseline plus separate treatment- and exposure-response pathways. Path-specific neighborhood contextualization supplies local pre-treatment information to the two pathways. We characterize the fitted response through a unique reference-normalized decomposition, which is equivalent to a zero mixed treatment--exposure contrast. Experiments on semi-synthetic benchmarks derived from BlogCatalog and Flickr show that NEDNet achieves the lowest PEHE among the compared methods for individual, peer, and total effects across homogeneous and covariate-heterogeneous response settings.
arxiv
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.
Provides a concrete methodological advance in causal inference for networked data, with benchmark results, useful for researchers and practitioners.
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