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Developments in genome-wide association studies and the increasingavailability of summary genetic association data have made the application oftwo-sample Mendelian Randomization (MR) with summary data increasingly popular.Conventional two-sample MR methods often employ the same sample for selectingrelevant genetic variants and for constructing final causal estimates. Such apractice often leads to biased causal effect estimates due to the well known"winner's curse" phenomenon. To address this fundamental challenge, we firstexamine its consequence on causal effect estimation both theoretically andempirically. We then propose a novel framework that systematically breaks thewinner's curse, leading to unbiased association effect estimates for theselected genetic variants. Building upon the proposed framework, we introduce anovel rerandomized inverse variance weighted estimator that is consistent whenselection and parameter estimation are conducted on the same sample. Underappropriate conditions, we show that the proposed RIVW estimator for the causaleffect converges to a normal distribution asymptotically and its variance canbe well estimated. We illustrate the finite-sample performance of our approachthrough Monte Carlo experiments and two empirical examples.

Breaking the Winner's Curse in Mendelian Randomization: Rerandomized Inverse Variance Weighted Estimator