Riesz Representers for the Rest of Us

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 is useful, using step-by-step worked examples.

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Publication Details

Journal
Epidemiology
Published
2026-05-21
DOI
https://doi.org/10.1097/ede.0000000000001996
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Riesz Representers for the Rest of Us

Nicholas Williams, Kara E. Rudolph, Oliver J. Hines
Epidemiology
Advanced Causal Inference Techniques
article

Riesz Representers for the Rest of Us

Nicholas Williams, Kara E. Rudolph, Oliver J. Hines
article en

Abstract

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 is useful, using step-by-step worked examples.

Epidemiology
Columbia University (US)
Openalex Percentile: Top 99%
Advanced Causal Inference Techniques
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Riesz Representers for the Rest of Us — Nicholas Williams, Kara E. Rudolph, et al. · Epidemiology (2026) | TGRS Research Map | TGRS