A method estimates unsigned CATE from residual outcome covariances to assist randomization tests without sample splitting, establishing identification, consistency, and validity while showing higher power in simulations.
Biometrika , volume =
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SelectiveRM applies optimal transport with a joint consistency discrepancy and partial mass relaxation to produce reward models that optimize a tighter upper bound on clean risk while autonomously dropping noisy preference samples.
The adaptive influence-based borrowing framework selects subsets of external controls by influence scores and chooses the subset minimizing MSE of the treatment effect estimator.
citing papers explorer
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Fit CATE Once: Model-Assisted Randomization Tests Without Sample Splitting
A method estimates unsigned CATE from residual outcome covariances to assist randomization tests without sample splitting, establishing identification, consistency, and validity while showing higher power in simulations.
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Optimal Transport for LLM Reward Modeling from Noisy Preference
SelectiveRM applies optimal transport with a joint consistency discrepancy and partial mass relaxation to produce reward models that optimize a tighter upper bound on clean risk while autonomously dropping noisy preference samples.
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Adaptive Influence-Based Borrowing Framework for Improving Treatment Effect Estimation in RCTs Using External Controls
The adaptive influence-based borrowing framework selects subsets of external controls by influence scores and chooses the subset minimizing MSE of the treatment effect estimator.