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Data-Adaptive Rerandomization for 2K Factorial Designs — arXiv:2609.02078

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.02078
License (as archived): CC0-1.0
Archived: 2026-10-17T05:16:59.606Z
SHA-256 of the archived text: dbdb27b02780319bf466fad9cb5ac4fd975622727685a0c4f1bb18caf57fd718
Stable URL of this page: https://metraipolis.com/documento/dc_2ff85169dfaa

How to cite

Data-Adaptive Rerandomization for 2K Factorial Designs — arXiv:2609.02078 — arXiv (en), https://arxiv.org/abs/2609.02078 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2ff85169dfaa (Metraipolis archive dc_2ff85169dfaa, sha256 dbdb27b02780319bf466fad9cb5ac4fd975622727685a0c4f1bb18caf57fd718)

BibTeX

@misc{dc2ff85169dfaa,
  title = {Data-Adaptive Rerandomization for 2K Factorial Designs — arXiv:2609.02078},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.02078},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2ff85169dfaa; sha256 dbdb27b02780319bf466fad9cb5ac4fd975622727685a0c4f1bb18caf57fd718},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.02078. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Factorial designs allow simultaneous estimation of multiple main effects and interactions, but covariate imbalance can substantially reduce estimation precision. Existing rerandomization methods improve covariate balance yet do not fully exploit heterogeneous priorities across factorial effects or effect-specific covariate importance. To address these limitations, this paper proposes a data-adaptive rerandomization framework for $2^K$ factorial designs. We first develop an oracle criterion that jointly incorporates researchers' priorities over factorial effects and effect-specific covariate importance, enabling precision gains with guaranteed lower bounds. To make the oracle criterion implementable, we develop a data-adaptive procedure that learns effect-specific covariate importance from a random subset of units and applies an estimated oracle criterion to the remaining units. Unlike existing two-stage rerandomization methods for treatment-control experiments, our procedure accommodates multiple factorial effects and requires no auxiliary dataset. Under a finite-population framework, we establish design-based asymptotic theory and show that the proposed procedure preserves the oracle design's precision-prioritization property and, under suitable conditions, achieves the same asymptotic precision as the oracle design. Numerical studies demonstrate substantial efficiency gains over existing rerandomization methods.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-03 in stat.ME).
Categorie arXiv: Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2609.02078 — 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 2026-10-17T04:27:19.628Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-09-03?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).