Evidence map›Paper›PMID 39976288›Full record

ArticleStatistics in medicine2025

Identification and Estimation of the Average Causal Effects Under Dietary Substitution Strategies.

Yu-Han Chiu, Lan Wen

Abstract read
In one paragraph

Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yu-Han ChiuDepartment of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, USA.
Lan WenCAUSALab and Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.ORCID 0000-0002-6120-8492

Funding

Long Term Multidisciplinary Study of Cancer in Women: The Nurses Health StudyUM1CA186107 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI ELIASSEN, A. HEATHER, STAMPFER, MEIR · 2014 to 2023
$22.3M
Uncovering Heterogeneity in Individual Treatment Responses via Causal Machine LearningR35GM154888 · NIGMS · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI Yu-Han Chiu · 2024 to 2026
$1.2M
Natural Sciences and Engineering Research Council of Canada DGECR-2023-00455Natural Sciences and Engineering Research Council of Canada RGPIN-2023-03641NCI NIH HHS UM1 CA186107NIGMS NIH HHS R35 GM154888NIGMS NIH HHS R35GM154888NIH HHS UM1 CA186107
6 · The paper itself

Abstract

The 2020-2025 Dietary Guidelines suggest that most people can improve their diet by making some changes to what they eat and drink. In many cases, these changes involve simple substitutions. For instance, the Dietary Guidelines recommend choosing chicken instead of processed red meat to reduce sodium intake and switching from refined grains to whole grains to increase dietary fiber intake. The question about such dietary substitution strategies seeks to estimate the average counterfactual outcome under a hypothetical intervention that replaces a food an individual would have consumed in the absence of intervention with a healthier substitute. In this work, we will show the conditions under which the average causal effects of substitution strategies can be non-parametrically identified, and provide efficient estimators for our proposed dietary substitution strategies. We evaluate the performance of our proposed methods via simulation studies and apply them to estimate the effect of substituting processed red meat with chicken on mortality, using data from the Nurses' Health Study.

Indexed as

CausalityDietAnimalsChickensComputer SimulationFemaleHumansModels, StatisticalNutrition PolicyRed Meatcausal effectsdouble robustnessinfluence functionssubstitution effectstargeted maximum likelihood estimation

Identifiers

PMID39976288
PMCPMC11840885

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.