Under instrumental-variable or negative-control assumptions, the authors prove a pessimism-based policy learning method achieves about 1/sqrt(n)-type regret for quantile reward objectives with unmeasured confounders.
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Quantile-Optimal Policy Learning under Unmeasured Confounding
Under instrumental-variable or negative-control assumptions, the authors prove a pessimism-based policy learning method achieves about 1/sqrt(n)-type regret for quantile reward objectives with unmeasured confounders.