# Causality in the Can: Diet Coke's Impact on Fatness

arXiv: https://arxiv.org/abs/2405.10746 · HTML: https://mlsanity.com/paper/2405.10746/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 17 May 2024.

## Builds on (most cited first, 6 of 6)

- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [Probabilities of Causation: Bounds and Identification](https://mlsanity.com/paper/1301.3898/) (2013)
- [Causes of Effects: Learning individual responses from population data](https://mlsanity.com/paper/2104.13730/) (2021)
- [Counterfactuals and Policy Analysis in Structural Models](https://mlsanity.com/paper/1302.4929/) (2013)
- [Probabilities of Causation: Adequate Size of Experimental and Observational Samples](https://mlsanity.com/paper/2210.05027/) (2022)
- [Unit Selection: Case Study and Comparison with A/B Test Heuristic](https://mlsanity.com/paper/2210.05030/) (2022)

## Abstract (arXiv)

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, was applied to estimate the RCT data. We then calculated the counterfactual quantity, the Probability of Necessity and Sufficiency (PNS), using both NHANES data and estimated RCT data. We propose that PNS is the essential metric for assessing the impact of Diet Coke on obesity. Our results indicate that between 20 to 50 percent of individuals, especially those with poor dietary habits, are more likely to gain weight from Diet Coke. Conversely, in groups like young females with healthier diets, only a small proportion experience weight gain due to Diet Coke. These findings highlight the influence of individual lifestyle and potential hormonal factors on the varied effects of Diet Coke, providing a new framework for understanding its nutritional impacts on public health.