Mendelian randomization (MR) is a method that uses genetic variants robustly associated with modifiable exposures to generate more reliable evidence on the causal effects of interventions on health outcomes. The approach is widely applied, and various methods to strengthen inference, such as two-sample MR, bidirectional MR, network MR, two-step MR, factorial MR, and multiphenotype MR, have been developed. MR leverages genetic information to improve the reliability of epidemiological studies, addressing issues like confounding, reverse causation, and biases. The review outlines the basic principles of MR, its analogy with randomized controlled trials, and recent extensions, including the use of multiple variants to increase power and test assumptions. It also discusses the challenges of pleiotropy and the complexity of associations in MR studies. MR has been successfully applied to a wide range of exposures and disease outcomes, and its scope is expected to expand with the decreasing cost of data generation.Mendelian randomization (MR) is a method that uses genetic variants robustly associated with modifiable exposures to generate more reliable evidence on the causal effects of interventions on health outcomes. The approach is widely applied, and various methods to strengthen inference, such as two-sample MR, bidirectional MR, network MR, two-step MR, factorial MR, and multiphenotype MR, have been developed. MR leverages genetic information to improve the reliability of epidemiological studies, addressing issues like confounding, reverse causation, and biases. The review outlines the basic principles of MR, its analogy with randomized controlled trials, and recent extensions, including the use of multiple variants to increase power and test assumptions. It also discusses the challenges of pleiotropy and the complexity of associations in MR studies. MR has been successfully applied to a wide range of exposures and disease outcomes, and its scope is expected to expand with the decreasing cost of data generation.