{
  "abstract": "We introduce Distributed Lag Neural Additive Models (DLNAMs), neural-additive analogues of Distributed Lag Non-linear Models (DLNMs) for learning nonlinear effects distributed over lags. DLNAMs replace a prespecified spline cross-basis with neural components that learn exposure--lag response surfaces, avoiding choices of basis family, dimension, and knot placement while preserving additive interpretability and familiar distributed-lag summaries. Exp-centered input layers, smooth activations, and learned subnetwork mixtures produce smooth, locally adaptive representations; pointwise uncertainty combines a conditional last-layer Laplace approximation with between-member ensemble variation. In simulations, DLNAMs generally outperformed DLNM comparators, including penalized and treed variants, in recovering known response functions, with lower bias, stronger boundary recovery, and better-calibrated cumulative intervals; gains were largest for more demanding functions. The architecture performed consistently across sample sizes, outcome families, lag horizons, and jointly fitted multi-exposure settings, retaining recovery performance as exposures were added; fit-specific changes were largely confined to optimization, and applications recovered established empirical patterns.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.07381",
  "author_total": 4,
  "authors": [
    {
      "name": "Calle Helmersson"
    },
    {
      "name": "Shivang Pandey"
    },
    {
      "name": "Leonardo Olivetti"
    },
    {
      "name": "Elena Raffetti"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9k0a598ed6jtq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.07381",
  "fulltext_url": null,
  "id": "9k0a598ed6jtq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.07381",
  "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",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "2613|Statistical Methods in Epidemiology"
    ]
  ],
  "openalex": "W7211990062",
  "pdf_url": "https://arxiv.org/pdf/2609.07381",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9k0a598ed6jtq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:07Z",
      "native_id": "2609.07381",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.07381"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Distributed Lag Neural Additive Models",
  "title_text": "Distributed Lag Neural Additive Models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:07Z",
  "url": "https://research.draconitic.com/paper/9k0a598ed6jtq",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.5281/zenodo.22288964",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}