Introduces policy-coupled coverage for conformal prediction in counterfactual decisions and the PC-RACP procedure that achieves higher utility with finite-sample coverage guarantees.
Decision theoretic foundations for conformal prediction: Optimal uncertainty quantification for risk-averse agents
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
The paper develops set-valued policies and conformal policy learning methods that output treatment sets with marginal coverage guarantees for robust decision-making under uncertainty.
citing papers explorer
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Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction
Introduces policy-coupled coverage for conformal prediction in counterfactual decisions and the PC-RACP procedure that achieves higher utility with finite-sample coverage guarantees.
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Set-Valued Policy Learning
The paper develops set-valued policies and conformal policy learning methods that output treatment sets with marginal coverage guarantees for robust decision-making under uncertainty.