This paper discusses the connection between regularization and causality, resting on the simple problem of linear regression, using ridge regression and Lasso as illustrative cases study for their argument. The paper provides original insights on the link between both regularization and causality. For the final version, it would be nice if the authors could introduce a bit more context on do-calculation (two lines stating that this is a pivotal tool from the framework of causality) and give more practical insights on the consequences of their results.