{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.07879",
  "author_total": 6,
  "authors": [
    {
      "name": "Zhirong Huang"
    },
    {
      "name": "Wenzhen Zhang"
    },
    {
      "name": "Guangquan Lu"
    },
    {
      "name": "Boyan Chen"
    },
    {
      "name": "Debo Cheng"
    },
    {
      "name": "Shichao Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d5ap8m7t6kj7m.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.07879",
  "fulltext_url": null,
  "id": "d5ap8m7t6kj7m",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.07879",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "stat.ML"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.07879",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d5ap8m7t6kj7m.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T18:21:40Z",
      "native_id": "2611.07879",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.07879"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Network Effect Decomposition for Causal Effect Estimation under Network Interference",
  "title_text": "Network Effect Decomposition for Causal Effect Estimation under Network Interference",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:52Z",
  "url": "https://research.draconitic.com/paper/d5ap8m7t6kj7m",
  "venue": "arXiv",
  "versions": []
}