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Balancing Covariates for Continuous Treatments by Exploiting Continuity — arXiv:2610.41186

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41186
License (as archived): CC0-1.0
Archived: 2026-11-02T04:55:26.554Z
SHA-256 of the archived text: 98507518b976714a788d4e67352b4a2e22ba2e9b4239bc00fc334b4732a7c57e
Stable URL of this page: https://metraipolis.com/documento/dc_dfeba3abb4d3

How to cite

Balancing Covariates for Continuous Treatments by Exploiting Continuity — arXiv:2610.41186 — arXiv (en), https://arxiv.org/abs/2610.41186 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dfeba3abb4d3 (Metraipolis archive dc_dfeba3abb4d3, sha256 98507518b976714a788d4e67352b4a2e22ba2e9b4239bc00fc334b4732a7c57e)

BibTeX

@misc{dcdfeba3abb4d3,
  title = {Balancing Covariates for Continuous Treatments by Exploiting Continuity — arXiv:2610.41186},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41186},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dfeba3abb4d3; sha256 98507518b976714a788d4e67352b4a2e22ba2e9b4239bc00fc334b4732a7c57e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41186. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We consider causal effect estimation in observational studies with continuous treatment. We introduce a general class of causal estimands that, as a special case, includes the average causal effect of assigning treatment according to an arbitrary counterfactual distribution. Balancing estimators are particularly attractive tools for covariate adjustment in this setting because they avoid directly modeling the treatment assignment mechanism, thereby reducing the potential for instability and misspecification bias in subsequent causal effect estimation. In this paper, we develop a general framework of covariate balancing for continuous treatment with the proposed class of causal estimands, leveraging the assumption of continuity of treatment dose. As in the binary treatment setting, analysts specify the basis functions to be balanced, the allowable degree of covariate imbalance, and a criterion for selecting among admissible weights. Under a minimum variance criterion for choosing weights, we establish consistency, characterize the effect of basis misspecification, and prove asymptotic normality. We develop consistent variance estimation, which yields asymptotically valid confidence intervals. Under homoskedasticity, our estimator is provably more efficient than inverse probability weighting based on the \emph{true} propensity score. Simulation studies support our theoretical results, and we illustrate the proposed method by estimating the effect of vocational training hours on expected future earnings using data from the U.S. Job Corps study.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ME, nuovi lavori del giorno).
Categorie arXiv: Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2610.41186 — 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 2026-11-02T04:46:12.630Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).