A doubly robust estimator is developed for quantile treatment effects on long-term outcomes by integrating randomized trial data with observational data under surrogate transportability, remaining consistent if either nuisance function is correctly estimated.
Journal of Machine Learning Research , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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DQPOPE estimates the entire return distribution in off-policy evaluation via deep quantile process regression, providing statistical advantages over standard single-value methods with equivalent sample sizes.
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Double/debiased machine learning of quantile treatment effects on long-term outcomes in clinical trials
A doubly robust estimator is developed for quantile treatment effects on long-term outcomes by integrating randomized trial data with observational data under surrogate transportability, remaining consistent if either nuisance function is correctly estimated.
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Distributional Off-Policy Evaluation with Deep Quantile Process Regression
DQPOPE estimates the entire return distribution in off-policy evaluation via deep quantile process regression, providing statistical advantages over standard single-value methods with equivalent sample sizes.