The paper discusses the Mendelian randomization-Egger (MR-Egger) method, a statistical approach used to assess causal relationships between risk factors and outcomes using genetic data. MR-Egger consists of three parts: testing for directional pleiotropy, testing for a causal effect, and estimating the causal effect. Unlike conventional Mendelian randomization methods that assume all genetic variants satisfy the instrumental variable assumptions, MR-Egger can handle genetic variants with pleiotropic effects by making a weaker assumption, the INSIDE (Instrument Strength Independent of Direct Effect) assumption. The authors critically evaluate the implementation and interpretation of MR-Egger, highlighting potential biases and inflated Type I error rates due to violations of the INSIDE assumption and the influence of outlying variants. They provide examples where conventional Mendelian randomization methods and MR-Egger yield different results and discuss how to interpret these findings. The paper emphasizes that MR-Egger is a valuable sensitivity analysis but should be used with caution, as it may not always provide reliable causal estimates. The authors also explore alternative robust methods for sensitivity analysis in Mendelian randomization, such as median-based methods and overidentification methods, and discuss the importance of using multiple methods to corroborate causal findings.The paper discusses the Mendelian randomization-Egger (MR-Egger) method, a statistical approach used to assess causal relationships between risk factors and outcomes using genetic data. MR-Egger consists of three parts: testing for directional pleiotropy, testing for a causal effect, and estimating the causal effect. Unlike conventional Mendelian randomization methods that assume all genetic variants satisfy the instrumental variable assumptions, MR-Egger can handle genetic variants with pleiotropic effects by making a weaker assumption, the INSIDE (Instrument Strength Independent of Direct Effect) assumption. The authors critically evaluate the implementation and interpretation of MR-Egger, highlighting potential biases and inflated Type I error rates due to violations of the INSIDE assumption and the influence of outlying variants. They provide examples where conventional Mendelian randomization methods and MR-Egger yield different results and discuss how to interpret these findings. The paper emphasizes that MR-Egger is a valuable sensitivity analysis but should be used with caution, as it may not always provide reliable causal estimates. The authors also explore alternative robust methods for sensitivity analysis in Mendelian randomization, such as median-based methods and overidentification methods, and discuss the importance of using multiple methods to corroborate causal findings.