Causal discovery is a challenging task in machine learning and statistics, as it involves inferring causal relationships between variables from data. Traditional causal discovery methods often require strong assumptions on the data distribution, such as non-Gaussianity. However, in many real-world scenarios, multiple related views of the same system are available, which can provide complementary information for causal discovery. In this research, the authors propose a novel approach for causal discovery in multi-view settings without relying on non-Gaussianity assumptions. The key idea is to leverage the multi-view structure of the data by introducing a multi-view linear Structural Equation Model (SEM). This model extends the traditional framework of causal discovery by incorporating correlations between different views of the data. One of the main advantages of the proposed multi-view causal discovery method is its ability to achieve identifiability without the need for strong assumptions on the data distribution. By leveraging the correlation between views, the proposed algorithm can infer causal relationships between variables more effectively, even in the absence of non-Gaussianity. Overall, this research contributes to the field of causal discovery by demonstrating that leveraging multi-view data can lead to more robust and reliable causal inference, especially in scenarios where traditional methods relying on non-Gaussianity assumptions may not be applicable. The proposed algorithm opens up new possibilities for causal discovery in complex systems with multiple related data sources.