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arXiv Machine Learning By Harvey Barnhard, Giacomo Opocher, Rahul Singh

Stable Policy Learning

Read the original on arXiv Machine Learning →

The paper investigates how policy learning algorithms should balance expected welfare against sampling risk in evidence-based policymaking. It demonstrates that algorithmic stability—specifically, a policy’s insensitivity to the replacement of a single experimental unit—limits sampling risk. The authors introduce policy‑vote bagging, which trains on many subsamples and averages their votes, preserving expected welfare while improving expected utility for risk‑averse researchers, and provide sharp bounds linking estimation accuracy, subsample size, and welfare variation, including an exact guarantee under CARA utility.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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