Investigating the Combined Application of Mendelian Randomization and Constraint-Based Causal Discovery Methods

Abstract

Mendelian randomization (MR) is often used in medical studies and biostatistics, to reveal direct causation effects between exposures and diseases, typically the effect of some exposure (like chemicals, habits and other factors) to a known disease or disorder. However, this procedure has some strict prerequisites, which often do not comply with the known variables, or the exact causal structure of the variables is not known in advance. In this study, we investigate the use of constraint-based causal discovery algorithms (PC, FCI and RFCI) to produce a sufficient causal structure from the known observations, to aid us in finding variable triplets, upon which MR can be performed. In addition, we show that the validity of MR cannot always be determined based on its results alone. Finally, we investigate the application of the MR principle to determine the direction of causality between variable-pairs, which is a problem most constraint-based causal discovery methods struggle with.