Formalizes counterfactual individual harm in RL and introduces a two-stage policy learning method with finite-sample guarantees on sub-optimality gap and harm rate control.
Piette, Joshua R
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Introduces a taxonomy of model, feedback, and prediction uncertainty in sequential decisions and demonstrates that accounting for uneven uncertainty across groups can reduce outcome variance for disadvantaged populations while preserving institutional objectives.
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Counterfactually Safe Reinforcement Learning
Formalizes counterfactual individual harm in RL and introduces a two-stage policy learning method with finite-sample guarantees on sub-optimality gap and harm rate control.