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The problem
Intervention: Community-based childhood obesity prevention programs, analysed with a causal machine learning framework (causal discovery, causal inference and tree-based models) to estimate individual treatment effects.
Who it was tested with: More than 7000 children participating in seven community-based intervention programs..
Experience from people who deliver programmes: stories, case studies and lessons learned. Valuable for understanding how and why, and for spotting what to test next, but not a measure of effect.
"Using data from seven CBI programs involving more than 7000 participants" - the abstract describes pooled program data without stating a randomised or comparison-group design, so the lower tier is used.
The design is unclear from the abstract, so a lower tier was chosen. No effect sizes are reported, and the overall effect of the programs is not stated. The focus is on methods and heterogeneity, not a trial of one intervention.
Across countries and designs
1 study · 1 country
