Inference on Winners

Abstract: Many empirical questions concern target parameters selected through optimization. For example, researchers may be interested in the effectiveness of the best policy found in a randomized trial, or the best-performing investment strategy based on historical data. Such settings give rise to a winner’s curse, where conventional estimates are biased and conventional confidence intervals are unreliable. This paper develops optimal confidence intervals and median-unbiased estimators that are valid conditional on the target selected and so overcome this winner’s curse. If one requires validity only on average over targets that might have been selected, we develop hybrid procedures that combine conditional and projection confidence intervals to offer further performance gains relative to existing alternatives.

Bio: Isaiah Andrews is a Professor of Economics at Harvard University, a Research Associate at the National Bureau of Economic Research (NBER), a fellow of the Econometric Society, and a co-editor at the American Economic Review. He specializes in econometrics, and his research focuses on developing methods for inference that are robust to common problems in empirical work, including insufficiently informative data (weak identification) and model misspecification. He received a MacArthur fellowship in 2020 and the John Bates Clark Medal in 2021.