I will introduce the Bayesian perspective of causal inference based on the potential outcomes framework, reviewing the causal estimands, the assignment mechanism and the general structure of Bayesian inference of causal effects. I will then extend the discussion to complex settings with intermediate variables and present an application of Bayesian Principal Stratification analysis to treatment switching in clinical trials.