{
  "abstract": "We introduce GenAI-Powered Inference (GPI), a statistical framework for causal inference using unstructured data, including text and images. GPI leverages open-source pretrained Generative AI (GenAI) models-such as large language models and diffusion models-not only to generate unstructured data at scale but also to extract low-dimensional representations that are guaranteed to capture their underlying structure. Applying machine learning to these representations, GPI enables estimation of causal effects while quantifying estimation uncertainty. Unlike existing approaches to representation learning, GPI does not require fine-tuning of GenAI models, making it computationally efficient and broadly accessible. We illustrate the versatility of the GPI framework through three applications: 1) estimating the effects of Chinese social media censorship while adjusting for textual confounders, 2) isolating the impact of specific image features from that of other correlated features in the same image, and 3) assessing the persuasiveness of political rhetoric. An open-source software package is available for implementing GPI.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Government, Harvard University",
    "Department of Statistics, Harvard University",
    "John F. Kennedy School of Government, Harvard University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Kosuke Imai",
      "orcid": "0000-0002-2748-1022"
    },
    {
      "affs": [
        2
      ],
      "name": "Kentaro Nakamura",
      "orcid": "0009-0002-1573-9150"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/11gxw1deveptg.bib",
  "cited_by": 12,
  "doi": "10.1073/pnas.2530532123",
  "fulltext_url": null,
  "id": "11gxw1deveptg",
  "issns": [
    "0027-8424",
    "1091-6490"
  ],
  "landing_url": "https://doi.org/10.1073/pnas.2530532123",
  "language": "en",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2613|Advanced Causal Inference Techniques"
    ]
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  "openalex": "W7207852525",
  "pdf_url": null,
  "pmcid": "PMC13552841",
  "pmid": "42691082",
  "published": "2026-09-03",
  "publisher": "National Academy of Sciences",
  "ris": "https://research.draconitic.com/paper/11gxw1deveptg.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T13:20:36Z",
      "native_id": "W7207852525",
      "source": "openalex",
      "url": "https://doi.org/10.1073/pnas.2530532123"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "openalex:2613|Advanced Causal Inference Techniques",
      "subfield": "statistics"
    }
  ],
  "title": "Leveraging generative AI for causal inference with unstructured data",
  "title_text": "Leveraging generative AI for causal inference with unstructured data",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:00Z",
  "url": "https://research.draconitic.com/paper/11gxw1deveptg",
  "venue": "Proceedings of the National Academy of Sciences",
  "versions": []
}