In this course, you will learn how to model causality mathematically, reason formally about cause, effect, and counterfactuals, predict the consequences of actions and analyse data to answer questions of a causal nature. We will use two different probabilistic frameworks for modelling causality: causal Bayesian networks and structural causal models. Topics addressed will be causal modelling (definition of Markov kernels, conditional independences, causal Bayesian networks, structural causal models, marginalisation, confounders, selection bias, feedback loops, causal graphs, interventions, Markov properties), causal reasoning and estimation (intervention variables, do-calculus, counterfactuals, covariate adjustment, back-door criterion, identifiability), and causal discovery and estimation (randomized controlled trials, instrumental variables, local causal discovery, Y-structures, the FCI algorithm).