Over the past decade, Mendelian randomization has become an established tool for probing questions of causality when characterizing the etiology of disease (Burgess and Thompson, 2015; Davey Smith and Ebrahim, 2003). The requirement for such approaches stems from a fundamental limitation of observational data, namely that causation cannot automatically be inferred from an association between an exposure and a disease. The association could be due to unobserved confounding between the exposure and the outcome, or reverse causation (the outcome affects the exposure) (Davey Smith and Ebrahim, 2004). These limitations are generally of no consequence when the aim is merely to predict the likelihood of future outcomes. However, if an exposure has a noncausal association with an outcome, then public health or pharmaceutical interventions targeted at the exposure will realize no material benefit and represent a waste of resources.
