Conformal Policy Selection: When to Deploy AI Decisions for Targeted Intervention
Artificial-intelligence (AI) policies are increasingly used to make consequential decisions in medicine, finance, education, and public services, yet deploying them responsibly requires knowing not only whether a policy helps on average, but for whom. Existing tools for off-policy evaluation and safe policy learning provide population-level guarantees, but some individuals may experience adverse treatment effects. We recast targeted deployment as a multiple-testing problem: for each candidate unit, test whether the outcome under the AI policy would exceed a user-specified threshold, and control the false discovery rate (FDR) among the selected set. Our method, Conformal Policy Selection, ...
| Authors | Junyu Cao, Ruijiang Gao, Maytal Saar-Tsechansky et al. |
|---|---|
| Primary category | stat.ME |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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