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Bootstrapping the Cross-Validation Estimate
Cross-validation is a widely used technique for evaluating the performance of prediction models.
Estimation and Validation of Ratio-based Conditional Average Treatment Effects Using Observational Data
Motivated by the need of modeling the number of relapses in multiple sclerosis patients, where the ratio of relapse rates is a natural choice of the treatment effect, we propose to estimate the conditional average treatment effect (CATE) as the ratio of expected potential outcomes, and derive a doubly robust estimator of this CATE in a semiparametric model of treatment-covariate interactions.
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