In machine learning, there has been great progress in obtaining powerful predictive models, but these models rely on correlations between variables and do not allow for an understanding of the underlying mechanisms or how to intervene on the system for achieve a certain goal. The concepts of causality are fundamental to have levers for action, to formulate recommendations and to answer the following questions: ‘what would happen if we had acted differently?’ The idea is to search for ‘Human like AI’, to take reasonable, robust decisions in never in never experienced situations. In this tutorial, we will introduce causal inference to answer questions such as what is the effect of Hydrochloroquine on mortality? We will present techniques in the potential framework of Rubin to estimate the average treatment effect (propensity weighting, double robust methods) as well as heterogeneous treatment effect for personalized medicine. We will leverage powerful machine learning for statistical inference. We will also discuss the structural causal model framework of pearl and tackle the data fusion problem to combine observational and randomized control trial data.