Implement randomization methods for RCTs. Use when user mentions: random assignment, treatment allocation, stratification, blocking, permutation randomization, cluster randomization, randomization balance, misfit handling, re-randomization.
The exact command may vary by repository. Check the README on GitHub.
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<skill_content>
<overview>
Randomization is the foundation of experimental causal inference. Proper implementation requires setting seeds once, documenting procedures, saving assignments immediately, and never re-running randomization to "improve" balance. Stratification improves precision when strata correlate with outcomes. Cluster randomization prevents spillovers but reduces power through design effects.
Randomization must be reproducible, documented, and executed exactly once per study.
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[Description truncada. Veja o README completo no GitHub.]
