{
  "abstract": "Businesses regularly offer gift card programs to drive customer spending and increase engagement. A central question is how much incremental revenue these programs generate, and which channels drive it most efficiently. Measuring the incremental revenue associated with a gift card program is a challenging problem in causal inference, requiring a firm to infer how much each customer would have spent if they never received a gift card. Observational data on past customer purchasing behavior reveal possession of a gift card only when a customer makes a purchase, thus leaving a customer's treatment status systematically censored. In this paper, we develop a novel data fusion approach to overcome this missing data challenge. We identify and estimate incrementality by combining a large observational dataset with a smaller experimental dataset from a different population. Our approach relies on a mild transferability condition, which posits that the conditional relative treatment effect of gift card receipt on the decision to purchase is invariant across the two populations. We develop a flexible, machine learning-based estimator for the incremental revenue and establish its asymptotic normality. We apply our estimator across both first- and third-party channels through which Airbnb distributes gift cards, finding heterogeneity in incrementality across segments of the population. In particular, we find not only that third-party channels are more incremental than first-party ones, but also that \"self-gifters\" (i.e., customers likely to have purchased their own gift cards) are more incremental than the broader population.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.08558",
  "author_total": 6,
  "authors": [
    {
      "name": "Justin Whitehouse"
    },
    {
      "name": "William Betz"
    },
    {
      "name": "Yan Zhang"
    },
    {
      "name": "Peter Coles"
    },
    {
      "name": "Ramesh Johari"
    },
    {
      "name": "Vasilis Syrgkanis"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3h9km0g6nxswa.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.08558",
  "fulltext_url": null,
  "id": "3h9km0g6nxswa",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.08558",
  "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",
      "econ.EM"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Advanced Causal Inference Techniques"
    ]
  ],
  "openalex": "W7220908037",
  "pdf_url": "https://arxiv.org/pdf/2610.08558",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3h9km0g6nxswa.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:32Z",
      "native_id": "2610.08558",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.08558"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Measuring Gift Card Program Incrementality via Causal Data Fusion",
  "title_text": "Measuring Gift Card Program Incrementality via Causal Data Fusion",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:32Z",
  "url": "https://research.draconitic.com/paper/3h9km0g6nxswa",
  "venue": "arXiv",
  "versions": []
}