This chapter introduces causal graphical models. We start with a review of the required background on probability theory and graph theory. Then, we present a general overview of Bayesian networks, including representation and reasoning. Next, we describe causal Bayesian networks, as an extension of Bayesian networks for causal modeling and reasoning about interventions (2nd level in the ladder of causation). Finally, we introduce structural equation models, required for counterfactual reasoning (3rd level in the ladder of causation).