This course is an introduction to modern causal inference theory and methods applicable to clinical and epidemiological research. The presentation is mathematically precise, but accessible to students of various disciplines of health sciences with some previous background in biostatistical and epidemiological methods as specified above. This course will introduce students to the potential outcome (counterfactual) model for causation, with brief forays into alternative conceptual models such as causal diagrams, structural equations and target trials. The statistical methods covered in the point treatment setting include propensity score estimation, direct standardization/g-computation, marginal structural models estimated using inverse probability of treatment weighting, doubly robust estimation, instrumental variables, and principal stratification. We will also cover extensions to causal inference from longitudinal data subject to time-dependent treatment-confounder feedback, mediation analysis, sensitivity analysis for unmeasured confounding, and use of machine learning methods in causal inference. R statistical environment is used for instruction.