{
  "abstract": "Without interference, cross-fitting enables flexible covariate adjustment while preserving finite-sample unbiasedness under independent unit-level randomization. Under network interference, out-of-sample prediction alone no longer guarantees unbiasedness: assignments entering evaluation-fold Horvitz--Thompson weights may also affect outcomes in the training sample, inducing dependence between fitted predictions and those weights. We develop neighborhood-excluded cross-fitting, which constructs estimand- and design-specific training samples to restore the conditional independence needed for finite-sample unbiasedness without a correctly specified outcome model. We establish asymptotically valid design-based Wald inference for direct and indirect effects under Bernoulli randomization and for the global average treatment effect under Bernoulli cluster randomization. Neighborhood exclusion creates a trade-off in choosing the number of folds: unit-level splitting may require the number of folds to grow with average exclusion-neighborhood size, while cluster-level splitting can substantially relaxes this requirement, permitting a fixed number of folds under partial interference. For linear adjustment under Bernoulli randomization, we derive variance-optimal and confidence-interval-length-optimal procedures, establish explicit rate conditions allowing the covariate dimension to diverge, and show that the variance-optimal procedure is asymptotically no-harm. Simulations illustrate the bias from omitting neighborhood exclusion and the precision gains from adjustment. An application to a social network experiment yields confidence intervals substantially shorter than those from the unadjusted estimator.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.23284",
  "author_total": 3,
  "authors": [
    {
      "name": "Haoyang Yu"
    },
    {
      "name": "Anqi Zhao"
    },
    {
      "name": "Hanzhong Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2y8vh3zcrbrsp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.23284",
  "fulltext_url": null,
  "id": "2y8vh3zcrbrsp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.23284",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Advanced Causal Inference Techniques"
    ]
  ],
  "openalex": "W7214012681",
  "pdf_url": "https://arxiv.org/pdf/2609.23284",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2y8vh3zcrbrsp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:33Z",
      "native_id": "2609.23284",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.23284"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Unbiased Treatment Effect Estimation under Network Interference via Neighborhood-Excluded Cross-Fitting",
  "title_text": "Unbiased Treatment Effect Estimation under Network Interference via Neighborhood-Excluded Cross-Fitting",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:33Z",
  "url": "https://research.draconitic.com/paper/2y8vh3zcrbrsp",
  "venue": "arXiv",
  "versions": []
}