Two methods are introduced to learn plug-in composite surrogates that maximize effect predictiveness, with the direct surrogate-effect modeling approach outperforming baselines on synthetic data with known effects and real-world experiment data.
Criteria for surrogate end points.Journal of the Royal Statistical Society Series B: Statistical Methodology, 69(5):919–932
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Learning plug-in surrogate endpoints for randomized experiments
Two methods are introduced to learn plug-in composite surrogates that maximize effect predictiveness, with the direct surrogate-effect modeling approach outperforming baselines on synthetic data with known effects and real-world experiment data.