{
  "abstract": "We introduce a class of marginal Structural Nested Mean Models (mSNMMs) that enable modeling time-varying effect modification by a subset of covariates required as adjustment variables. We focus on identifiability and estimation under conditional parallel trends assumptions, so mSNMMs are applicable in settings where time-varying Difference-in-Differences (DiD) methods are typically applied. Modeling effect modification by lower dimensional covariates reduces the risk of model misspecification and can improve interpretability relative to previously developed `full SNMMs'. mSNMMs enjoy two key advantages over alternative time-varying DiD methods. First, the parallel trends assumptions that they require are more plausible in the presence of time-varying confounding. Second, they are able to model effects of \\textit{sustained} interventions even when treatment switches on and off in the data. We illustrate the methodology by applying mSNMMs to estimate how the effects of Medicaid expansion (a time-varying treatment) on local uninsurance rates vary with unemployment rate prior to expansion (a time-varying covariate), adjusting for additional time-varying demographic and economic variables.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.22113",
  "author_total": 4,
  "authors": [
    {
      "name": "Oliver Dukes"
    },
    {
      "name": "Zach Shahn"
    },
    {
      "name": "James Robins"
    },
    {
      "name": "Andrea Rotnitzky"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cst9bht85g3en.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.22113",
  "fulltext_url": null,
  "id": "cst9bht85g3en",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.22113",
  "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": "W7222569571",
  "pdf_url": "https://arxiv.org/pdf/2610.22113",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cst9bht85g3en.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:00Z",
      "native_id": "2610.22113",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.22113"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Difference-in-Differences for Heterogeneous Effects of General Treatment Strategies Accounting for Time-Varying Covariates",
  "title_text": "Difference-in-Differences for Heterogeneous Effects of General Treatment Strategies Accounting for Time-Varying Covariates",
  "type": "preprint",
  "updated_at": "2026-10-30T12:30:00Z",
  "url": "https://research.draconitic.com/paper/cst9bht85g3en",
  "venue": "arXiv",
  "versions": []
}