Triage scores extend risk scores via additive counterfactual utilities to incorporate intervention effects in high-stakes decisions.
arXiv preprint arXiv:2305.11812 , year=
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Establishes finite-sample regret bounds of order sqrt(N-dim(Π)/N) for IPW and DR estimators in Wasserstein policy learning with distributional outcomes, plus a matching minimax lower bound.
Derives optimal logging policies for minimizing off-policy evaluation error under known, unknown, and partially known target policies and reward distributions.
citing papers explorer
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Triage Score: A Counterfactual Risk Assessment Instrument
Triage scores extend risk scores via additive counterfactual utilities to incorporate intervention effects in high-stakes decisions.
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Wasserstein Policy Learning for Distributional Outcomes
Establishes finite-sample regret bounds of order sqrt(N-dim(Π)/N) for IPW and DR estimators in Wasserstein policy learning with distributional outcomes, plus a matching minimax lower bound.
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Logging Policy Design for Off-Policy Evaluation
Derives optimal logging policies for minimizing off-policy evaluation error under known, unknown, and partially known target policies and reward distributions.