Causality in the Can: Diet Coke's Impact on Fatness
Yicheng Qi, Ang Li

TL;DR
This paper applies causal inference methods to observational and RCT data to assess the impact of Diet Coke on obesity, revealing significant individual variability influenced by lifestyle and hormonal factors.
Contribution
It introduces a causal inference framework using structural causal models and PNS to evaluate Diet Coke's effect on weight gain, moving beyond association-based analyses.
Findings
20-50% of individuals with poor diets may gain weight from Diet Coke
Young females with healthy diets are less affected by Diet Coke
Causal approach reveals individual differences in Diet Coke's impact
Abstract
Artificially sweetened beverages like Diet Coke are often considered better alternatives to sugary drinks, but the debate over their impact on health, particularly in relation to obesity, continues. Previous research has predominantly used association-based methods with observational or Randomized Controlled Trial (RCT) data, which may not accurately capture the causal relationship between Diet Coke consumption and obesity, leading to potentially limited conclusions. In contrast, we employed causal inference methods using structural causal models, integrating both observational and RCT data. Specifically, we utilized data from the National Health and Nutrition Examination Survey (NHANES), which includes diverse demographic information, as our observational data source. This data was then used to construct a causal graph, and the back-door criterion, along with its adjustment formula,…
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Taxonomy
TopicsDiet, Metabolism, and Disease
MethodsCausal inference
