Causal Inference and Causal Diagrams in Big, Real-World Observational Data and Pragmatic Trials

Go Back

This course will introduce innovative causal inference approaches for the design and analysis of big real-world observational data and pragmatic trials. Topics include causal diagrams (DAGs), target trial emulation, and causal methods for time-independent confounding (multivariate regression, propensity scores) and time-dependent confounding (g-methods), illustrated using clinical case examples.