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Testing Treatment Effect Heterogeneity with Functional Outcomes — arXiv:2610.23397

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.23397
License (as archived): CC0-1.0
Archived: 2026-10-19T09:37:38.607Z
SHA-256 of the archived text: a4fda2c0b6703f15f372e53441141295589457289486a5681e1d10fd2c96ffbb
Stable URL of this page: https://metraipolis.com/documento/dc_4264bd79bf43

How to cite

Testing Treatment Effect Heterogeneity with Functional Outcomes — arXiv:2610.23397 — arXiv (en), https://arxiv.org/abs/2610.23397 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4264bd79bf43 (Metraipolis archive dc_4264bd79bf43, sha256 a4fda2c0b6703f15f372e53441141295589457289486a5681e1d10fd2c96ffbb)

BibTeX

@misc{dc4264bd79bf43,
  title = {Testing Treatment Effect Heterogeneity with Functional Outcomes — arXiv:2610.23397},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.23397},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4264bd79bf43; sha256 a4fda2c0b6703f15f372e53441141295589457289486a5681e1d10fd2c96ffbb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.23397. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Assessing treatment-effect heterogeneity is important in causal inference, particularly for functional outcomes where effects may vary over time and the common effect is unknown. We propose a nonparametric procedure that assesses treatment-effect homogeneity through a testable marginal implication. By comparing within-arm difference distributions, the construction removes the unknown common functional effect without estimating it. We measure departures between these distributions through an integrated squared discrepancy of their characteristic functionals. A full-support Gaussian integrating measure yields an explicit bounded-kernel representation and identifies equality of the difference laws without requiring moment conditions on the outcomes. We construct an unbiased generalized U-statistic that accounts for dependence among overlapping differences, derive its null, fixed-alternative, and local-alternative limits, and develop a centered multiplier bootstrap for inference. The framework extends to multiple treatment arms with a shared control. For computation, a random-direction approximation combined with a nine-state recursion reduces the cost to linear order in the sample size for fixed numerical settings. Simulation studies assess the finite-sample performance of the proposed procedure relative to several benchmark methods. An application to the Irish gas smart-metering trial illustrates its use for assessing heterogeneity in functional treatment responses.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ME, nuovi lavori del giorno).
Categorie arXiv: Methodology (stat.ME) ; Statistics Theory (math.ST).
Pagina del lavoro: https://arxiv.org/abs/2610.23397 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T09:32:59.738Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).