Riesz representers for the rest of us

Fuente: arXiv
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Auteurs principaux: Williams, Nicholas T., Hines, Oliver J., Rudolph, Kara E.
Format: Preprint
Publié: 2025
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author Williams, Nicholas T.
Hines, Oliver J.
Rudolph, Kara E.
author_facet Williams, Nicholas T.
Hines, Oliver J.
Rudolph, Kara E.
contents The application of semiparametric efficient estimators, particularly those that leverage machine learning, is rapidly expanding within epidemiology and causal inference. This literature is increasingly invoking the Riesz representation theorem and Riesz regression. This paper aims to introduce the Riesz representation theorem to an epidemiologic audience, explaining what it is and why it's useful, using step-by-step worked examples.
format Preprint
id arxiv_https___arxiv_org_abs_2507_19413
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Riesz representers for the rest of us
Williams, Nicholas T.
Hines, Oliver J.
Rudolph, Kara E.
Statistics Theory
The application of semiparametric efficient estimators, particularly those that leverage machine learning, is rapidly expanding within epidemiology and causal inference. This literature is increasingly invoking the Riesz representation theorem and Riesz regression. This paper aims to introduce the Riesz representation theorem to an epidemiologic audience, explaining what it is and why it's useful, using step-by-step worked examples.
title Riesz representers for the rest of us
topic Statistics Theory
url https://arxiv.org/abs/2507.19413