Similar to inverse variance weighted (IVW) method but uses a simulation-based approach to detect outlying variants and flags them for removal in order to re-estimate the original association between the exposure and outcome. It has been shown that this outlier detection framework is closely related to the analytical approach of applying IVW with so-called “modified second order weights”.
Outlier removal methods can effectively reduce bias in MR estimates, but caution must be made because they will necessarily reduce the standard error and could lead to overfitting. Other outlier removal methods have also been implemented elsewhere in generalized summary MR (GSMR) method and Radial MR. See NOME adjustment for more information about such "modified-second order weights" and other related weights.