Artificial intelligence’s primary engine, deep learning, has several issues with regard to its data-hungry nature along with a lack of interpretability and explainability. A principled approach to overcome these weaknesses is causal modelling and inference, a mathematical framework well aligned with human-like cognition. In this course, we will show how causality can help machine learning models ascend the ladder of causation, moving beyond mere identification of statistical associations (rung 1 inferences) to provide more insightful and valuable interventional and counterfactual explanations (rung 2 and 3 inferences). Then after covering the identification and estimation of causal effects we will present the current state of research in causality, eventually concluding with a hands-on session where the participants can do a practical deep dive into causal models.