Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction
Predictions are increasingly used to guide high-stakes decisions, from treatment selection to policy making. To ensure reliability with imperfect predictions, uncertainty quantification methods such as conformal prediction build prediction sets with coverage guarantees. However, statistical validity alone does not immediately determine the decisions to take, nor the optimality thereof. This gap is especially delicate in counterfactual settings where the outcome that materializes depends on the action taken, so uncertainty cannot be specified independently of the decision rule. We develop a decision-theoretic framework for uncertainty-informed counterfactual decisions. We identify a novel ...
| Authors | Yurui Zheng, Ying Jin |
|---|---|
| Primary category | stat.ML |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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