{
  "abstract": "In observational time series, statistical inference for dynamic causal effects of a one-time intervention across horizons is complicated by high-dimensional observed pre-treatment information, unmeasured confounding, and serial dependence. To address these challenges, we develop a semiparametric framework for inference from a single serially dependent time series, integrating debiased machine learning with instrumental variables through buffered block cross-fitting. Under geometric beta-mixing, we derive non-asymptotic bounds on estimation error, asymptotic normality at each fixed horizon, and feasible inference that accommodates serial dependence. We further show how learner-specific prediction guarantees under temporal dependence can be used to verify the nuisance-rate conditions required for orthogonal inference. In a monetary-policy application with 468 months and 1464 lagged FRED-MD controls, we show an instrumented policy tightening lowers housing starts at medium horizons, with sensitivity analyses that support the finding.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.27531",
  "author_total": 4,
  "authors": [
    {
      "name": "Shibo Yu"
    },
    {
      "name": "Yan Chen"
    },
    {
      "name": "Jin-Hong Du"
    },
    {
      "name": "Guodong Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bp811vz6mdttw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.27531",
  "fulltext_url": null,
  "id": "bp811vz6mdttw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.27531",
  "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": "W7214176826",
  "pdf_url": "https://arxiv.org/pdf/2609.27531",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bp811vz6mdttw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:06Z",
      "native_id": "2609.27531",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.27531"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Semiparametric Inference for Dynamic Causal Effects from Observational Time Series",
  "title_text": "Semiparametric Inference for Dynamic Causal Effects from Observational Time Series",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:06Z",
  "url": "https://research.draconitic.com/paper/bp811vz6mdttw",
  "venue": "arXiv",
  "versions": []
}