One of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is of utmost importance. Fundamentals of Causal Inference: With R explains and relates different methods of confounding adjustment in terms of potential outcomes and graphical models, including standardization, doubly robust estimation, difference-in-differences estimation, front-door estimation, and instrumental variables estimation. These methods are compared in terms of estimating the average effect of treatment on the treated (ATT). The fundamentals of mediation analysis and adjusting for time-dependent confounding are also presented. Several real data examples, simulation studies, and analyses using R motivate and illustrate the methods throughout. The course assumes familiarity with basic statistics and probability, regression, and R. The course will be taught with a blend of lecture, worked examples and hands-on examples in R.