{
  "abstract": "We develop the first statistical methodology for causal inference with video features as treatments. Video is the most engaging content modality on the internet. A central causal question is how audience reactions change in response to treatment features that unfold over the course of a video. Unfortunately, standard causal inference methods are not applicable because confounding features are latent, high-dimensional, and dynamically related to both the treatment sequence and the outcome trajectory. To address these challenges, we first reproduce each video using a deep generative model and leverage the model's internal representations as learned, low-dimensional summaries of video content for causal estimation. We then establish that the average potential-outcome trajectory under dynamic stochastic interventions is nonparametrically identified. Lastly, we propose a consistent and asymptotically normal estimator based on a longitudinal neural network architecture. We empirically validate our approach by constructing a new causal inference benchmark consisting of $10{,}000$ Super Mario Bros. levels played by fixed Mario AI agents, where ground-truth causal effects are known by construction. Finally, we apply our method to television advertisements from the 2020 U.S. presidential campaign and find that increasing the probability of a candidate appearing over time leads to higher average viewer evaluations. With the proposed methodology, researchers can ask which visual features, appearing at which points in a video, influence audience responses, while benchmarking new methods against datasets with known ground-truth causal effects.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.06126",
  "author_total": 5,
  "authors": [
    {
      "name": "Kentaro Nakamura"
    },
    {
      "name": "Adam Breuer"
    },
    {
      "name": "Michael H. Crespin"
    },
    {
      "name": "Bryce J. Dietrich"
    },
    {
      "name": "Kosuke Imai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8wfmksfz9qsvr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.06126",
  "fulltext_url": null,
  "id": "8wfmksfz9qsvr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.06126",
  "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.AP"
    ],
    [
      "openalex",
      "2613|Advanced Causal Inference Techniques"
    ]
  ],
  "openalex": "W7167716621",
  "pdf_url": "https://arxiv.org/pdf/2607.06126",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8wfmksfz9qsvr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:51Z",
      "native_id": "2607.06126",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.06126"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Causal Inference with Video Features as Treatments",
  "title_text": "Causal Inference with Video Features as Treatments",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:51Z",
  "url": "https://research.draconitic.com/paper/8wfmksfz9qsvr",
  "venue": "arXiv",
  "versions": []
}