In this course, we will start by reviewing the concepts and aims of epidemiology and its relationship with causal inference, then we move further on how genetics can help to learn causality in epidemiology. We will focus on Mendelian randomization (MR), which uses genetic variants as instrumental variables to test or estimate the potential causal effect of a (non-genetic) risk factor on a disease or health-related outcome. When the assumptions are met, the MR approach can overcome the limitations of associations drawn from observational epidemiology and help prioritize potential targets for pharmaceutical and public health interventions. This short course will provide all the tools necessary first to understand the basic principles of causal inference underlying MR and second to perform an MR study, covering both simple and complex statistical methods for causal inference within one- and two-sample Mendelian randomization frameworks. Further topics, such as genetic risk score analysis, LD score regression, and propensity score analysis will also be discussed in comparisons with the MR framework.