Adaptive Penalization and Bootstrap-Smoothed Inference for Two-Sample Mendelian Randomization with Summary Data
两样本孟德尔随机化的自适应惩罚与自助平滑推断
Two-sample Mendelian randomization (MR) uses genetic variants as instrumental variables to estimate causal effects from observational data using summary association statistics. However, horizontal pleiotropy can invalidate standard MR estimators and lead to biased causal inference. Pleiotropy-robust methods have been proposed to address this issue, including regularization-based approaches such as MR-Lasso. However, MR-Lasso may fail to identify invalid instruments…