A Causal Mediation Analysis for Investigating the Effect of a Randomized Cash-Transfer Program in Nicaragua
Bibliographic record
Abstract
Mediation analysis can be applied to data from randomized trials of health and social interventions to draw causal inference concerning their mechanisms. We used data from a cluster-randomized trial in Nicaragua, fielded between 2000 and 2002, to investigate whether the impact of providing access to a conditional cash-transfer program on child nutritional outcomes was mediated by child health check-ups and household dietary diversity. In a sample of 443 children 6-35 months old, we estimated the controlled direct (CDE) effect of random assignment on measured height-for-age z scores had we intervened so that all children received a health check-up and had the same level of household dietary diversity, using inverse-probability weighted marginal structural models to account for mediator-outcome confounding. Sensitivity analyses corrected the CDE for potential nondifferential error in the measurement of dietary diversity. Treatment assignment increased height-for-age z score by 0.37 (95% CI: 0.05, 0.69) standard deviations. The CDE was 0.20 (95% CI: -0.17, 0.57) standard deviations, suggesting nearly one-half of the program's impact on child nutrition would be eliminated had we intervened on these factors, although estimates were relatively imprecise. This study provides an illustration of how causal mediation analysis can be applied to examine the mechanisms of multifaceted 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 | 0.175 | 0.281 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".