Mendelian Randomization (MR) is a popular method in epidemiology and genetics that uses genetic variation as instrumental variables for causal inference. Existing MR methods usually assume most genetic variants are valid instrumental variables that identify a common causal effect. There is a general lack of awareness that this effect homogeneity assumption can be violated when there are multiple causal pathways involved, even if all the instrumental variables are valid. In this talk we will introduce a latent mixture model that groups instruments that yield similar causal effect estimates together. We will use a Monte-Carlo EM algorithm to fit this mixture model and then derive the approximate confidence intervals for uncertainty quantification. The concept of mechanistic heterogeneity and the proposed method will be illustrated with two real data examples—the effect of high-density lipoprotein cholesterol on coronary heart disease and the effect of adiposity on type II diabetes.