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The problem
Intervention: Daycare center closures of varying duration
Who it was tested with: 14,867 mothers and their children in Germany.
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.
"Our causal forest analysis reveals significant dose-response relationships: short closures reduce maternal well-being by -0.097 standard deviations (SD) (95% CI: [-0.136, -0.058]), while longer closures show stronger effects -0.192 SD (95% CI: [-0.261, -0.123])." The causal forest method with machine learning for causal inference and the large sample size constitute a quasi-experimental design examining the natural variation in closure timing driven by staffing constraints.
Study relies on mother-reported outcomes rather than independent child assessment; causality is inferred from observed covariates; effects may vary by unmeasured family characteristics.
Across countries and designs
1 study · 1 country
