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 even when common “reassurances” from the literature hold: training data come from randomized trials, estimated gains are large, and predictive models are accurate, well-calibrated, and…