Abstract: Data-driven decision-making relies on credible policy evaluation: we need to know whether a learned policy truly improves outcomes. This talk examines a key failure mode—the winner’s curse—where policy optimization exploits prediction error and selection, producing optimistic, often spurious performance gains. First, we show that model-based policy optimization and evaluation can report large, stable improvements…