Causal Analysis of Multidimensional Dietary Data to Assess Effects on All-Cause Mortality
Yohannes Adama Melaku, Zumin Shi · 2026
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Description
Melaku and Shi applied causal methods to dietary data and death risk in twenty twenty-six. Diet data are tangled: people eat many foods together, so isolating one food's effect is hard. The team used methods that state their causal assumptions openly rather than hiding them.
They analyzed twelve thousand six hundred thirty-five United States adults followed for fifteen years, during which four hundred died. Clustering sorted eaters into healthy, unhealthy, and mixed patterns. Weighting methods changed how those patterns linked to death, showing method choice matters. A multivariate score method found added sugar raised risk, while legumes lowered it. Whole grains showed benefit in a standard model. The authors warn the results are estimates under assumptions, not proof, and need testing in larger, more varied groups.
Catalog record
| CREATOR | Yohannes Adama Melaku, Zumin Shi |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |