Designing Experiments for External Decision Makers
Standard Bayesian experimental design (BED) methods assume the experimenter alone will act on the data, choosing the best action given their own beliefs and goals. In practice, data are often acted on by external parties whose procedures the experimenter cannot change and may not know, particularly when the data are released publicly. To extend BED to such settings, we introduce usage-aware experimental design, a framework in which an adaptive design policy is trained to minimise the usage risk: the experimenter's expected loss when one or more external rules act on the collected data. These rules may be non-Bayesian or black-box, and when several may apply, we either average over them or...
| Authors | Sahel Iqbal, Tom Rainforth |
|---|---|
| Primary category | stat.ML |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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