Sharp bounds are derived on the proportion of physicians whose personal strategies perform at least as well as the trial's better average treatment, using nested randomized and observational data from the same population.
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Counterfactual metrics on semi-simulated benchmarks fail to identify the treatment effect estimators preferred by observable metrics on real datasets, with simple meta-learners outperforming specialized causal models.
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Trust Me, I'm a Doctor?
Sharp bounds are derived on the proportion of physicians whose personal strategies perform at least as well as the trial's better average treatment, using nested randomized and observational data from the same population.
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Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation
Counterfactual metrics on semi-simulated benchmarks fail to identify the treatment effect estimators preferred by observable metrics on real datasets, with simple meta-learners outperforming specialized causal models.