Causal Effects of Low Income on Obesity: Business and Health Insights From a National Survey and Machine Learning Analysis With Applied Econometrics Technique
Bibliographic record
Abstract
This study investigates the causal impact of low income on Body Mass Index (BMI) using data from the 2017–2018 National Health and Nutrition Examination Survey (NHANES). While previous research has established a correlation between socioeconomic status and obesity, this study employs Double Machine Learning (DML) to identify causal effects, controlling for confounders such as age, gender, education, ethnicity, and household size. The full sample (n = 8,005) and two subgroups, high BMI and high BMI + low income, were analyzed. Results from DML indicate a statistically significant causal effect, with low-income status increasing BMI by approximately 0.49 units (p < 0.001). Subgroup analyses reveal that low-income individuals, especially older adults and females, face disproportionately higher obesity risks. These findings underscore the need for equity-centered public health strategies targeting the socioeconomic roots of obesity, including nutritional support, education, and community-based interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".