{
  "abstract": "Real-world public health policies are often enacted by local jurisdictions, but their effects can ripple through connected populations. For causal evaluation, this creates a difficult spatiotemporal problem: intermittent policy updates create path-level treatments, and because spillover depends on neighboring treatment paths along with the focal-unit's own history, controlling time-varying confounding requires high-dimensional, multimodal information from both sources. To address this challenge, we develop a neural spatiotemporal spillover framework that extends potential-outcome causal analysis to networked policy-systems with unit-level effects and structured interference. The framework indexes each unit's potential outcomes through own-treatment and neighborhood spillover-exposure paths, yielding direct, spillover, and total effects. We introduce an LSTM-based representation-learning encoder that integrates each unit's own and neighborhood histories into compact sequential summaries for downstream modeling and causal estimation. These summaries feed a self-normalized kernel inverse-probability-weighting estimator for continuous spillover exposure. Beyond theoretical guarantees, simulations show improved performance over outcome-regression baselines; in a practically motivated county-level COVID-19 policy study, the framework finds both business-and-economic restrictions and education-and-childcare policies associated with lower subsequent case burden, with larger estimates for business-and-economic restrictions. Overall, it provides a scalable, policy-interpretable framework for evaluating unit-level effects in high-dimensional, multimodal spatiotemporal systems when interventions spill over across space and time.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.16086",
  "author_total": 3,
  "authors": [
    {
      "name": "Shaohui Zhang"
    },
    {
      "name": "Jiasheng Shi"
    },
    {
      "name": "Jing Huang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1vjbj3tn5gd5k.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.16086",
  "fulltext_url": null,
  "id": "1vjbj3tn5gd5k",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.16086",
  "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": "W7221767599",
  "pdf_url": "https://arxiv.org/pdf/2610.16086",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1vjbj3tn5gd5k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:42Z",
      "native_id": "2610.16086",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.16086"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "When Treatments Spill Over: A Neural Framework for Spatiotemporal Causal Effect Estimation",
  "title_text": "When Treatments Spill Over: A Neural Framework for Spatiotemporal Causal Effect Estimation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:42Z",
  "url": "https://research.draconitic.com/paper/1vjbj3tn5gd5k",
  "venue": "arXiv",
  "versions": []
}