{
  "abstract": "Conditional independence (CI) is a fundamental concept in statistics and machine learning. Recent advances in conditional generative modeling provide flexible tools for generative-model-based CI tests, which rely on an estimated conditional distribution to generate randomized samples. However, errors in estimating this distribution accumulate in existing Type I error bounds, and consistency of the generative estimator alone does not guarantee asymptotic Type I error control. To address this limitation, we formulate conditional generative modeling as a domain adaptation problem and leverage auxiliary data from multiple source domains to improve estimation in the target CI testing domain. We propose Domain-Adapted Diffusion (DA-Diff), a multi-source domain adaptation framework for conditional diffusion models based on weighted empirical risk minimization over both target and source domains. We establish the convergence rate of DA-Diff and show how transferable source data can improve target-domain estimation through an increased effective sample size while controlling transfer bias. Building on DA-Diff, we further propose Domain-Adapted Conditional Independence Testing (DA-CIT) and show that its Type I error satisfies $P(p \\leq α) \\leq α+ o(1)$. Experiments demonstrate that DA-Diff improved conditional generation quality compared with transfer-learning diffusion baselines, while DA-CIT provides strong Type I error control and competitive power.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.33490",
  "author_total": 11,
  "authors": [
    {
      "name": "Yanfeng Yang"
    },
    {
      "name": "Junda Zhao"
    },
    {
      "name": "Yijie Gao"
    },
    {
      "name": "Jiaqi Yang"
    },
    {
      "name": "Xinyu Shi"
    },
    {
      "name": "Ziqi Chen"
    },
    {
      "name": "Shunyu Zhao"
    },
    {
      "name": "Shuai Li"
    },
    {
      "name": "Wei Huang"
    },
    {
      "name": "Eshant English"
    },
    {
      "name": "Kenji Fukumizu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/15ja5q4br7rwp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.33490",
  "fulltext_url": null,
  "id": "15ja5q4br7rwp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.33490",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Domain Adaptation and Few-Shot Learning"
    ]
  ],
  "openalex": "W7214745439",
  "pdf_url": "https://arxiv.org/pdf/2609.33490",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/15ja5q4br7rwp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:26Z",
      "native_id": "2609.33490",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.33490"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Domain-Adapted Diffusion Models for Conditional Independence Testing",
  "title_text": "Domain-Adapted Diffusion Models for Conditional Independence Testing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:26Z",
  "url": "https://research.draconitic.com/paper/15ja5q4br7rwp",
  "venue": "arXiv",
  "versions": []
}