Relaxing parametric assumptions for non-linear Mendelian randomization using a doubly-ranked stratification method
Haodong Tian, Amy M. Mason, Cunhao Liu, Stephen Burgess
This paper introduces a doubly-ranked stratification method for non-linear Mendelian randomization to avoid parametric assumptions, showing it provides unbiased estimates even with non-linear or heterogeneous instrument-exposure relationships.