Epidemiological and clinical studies have identified many physiological traits and biomarkers that are statistically associated with coronary artery disease (CAD). For some of these traits and biomarkers it is well established that they represent true causal risk factors for CAD. For other biomarkers, however, the distinct character of association is still a matter of debate. Randomized controlled trials (RCT) had a pivotal role in establishing causal associations between risk factors and biomarkers and CAD in some settings by demonstrating that therapeutic intervention targeting risk factors/biomarkers also affect the risk for clinical outcomes, such as CAD. In other scenarios, however, RCTs did not demonstrate clear benefits associated with lowering biomarker levels and therefore suggest that the association between these biomarkers (like C reactive protein) and CAD was driven by confounding or reverse causation. Even accurately conducted RCTs are not immune against incorrect causal inference. Moreover, the extensive costs and efforts required to conduct RCTs asked for alternative study designs to elucidate potential causal associations. Mendelian Randomization studies represent one such alternative by using genetic variants as proxies for specific biomarkers to investigate potential causal relations between biomarkers and clinical outcomes. In this review, we briefly describe the principles of MR studies and summarize recent MR studies in the context of CAD.