{
  "abstract": "We study the functional average treatment effect on the treated in a two-period difference-in-differences design. Under conditional functional parallel trends, we derive the full-curve efficient influence function and efficiency bound and construct a cross-fitted, doubly robust estimator. Under ultra-dense observation, bounds on curve-estimation error and learner stability preserve its full-data first-order behavior. Nuisance-rate and moment conditions yield asymptotic linearity in $L^2$; additional Sobolev regularity gives uniform Gaussian limits and valid continuous-domain bands computed on a refining grid. In simulations, estimation errors decrease with sample size under single-model misspecification, and bands accounting for nuisance estimation have near-nominal or conservative coverage. Further experiments examine basis-truncation bias and nuisance-estimation uncertainty. An application to electricity load profiles from 4389 eligible households estimates a modest reduction concentrated in evening consumption among dynamic-tariff participants.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.26964",
  "author_total": 3,
  "authors": [
    {
      "name": "Junzhu Nie"
    },
    {
      "name": "Chengxiu Ling"
    },
    {
      "name": "Mengfei Ran"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cpff8jcz3346q.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.26964",
  "fulltext_url": null,
  "id": "cpff8jcz3346q",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.26964",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Advanced Causal Inference Techniques"
    ]
  ],
  "openalex": "W7162554399",
  "pdf_url": "https://arxiv.org/pdf/2605.26964",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cpff8jcz3346q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:41Z",
      "native_id": "2605.26964",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.26964"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Semiparametric Inference for Functional Difference-in-Differences",
  "title_text": "Semiparametric Inference for Functional Difference-in-Differences",
  "type": "preprint",
  "updated_at": "2026-11-01T12:21:00Z",
  "url": "https://research.draconitic.com/paper/cpff8jcz3346q",
  "venue": "arXiv",
  "versions": []
}