Continues PHTH 6801. Expands on foundational knowledge of causal inference by examining time-varying exposures, introducing the g-formula for estimating standardized outcome distributions, and unraveling the intricacies of marginal structural models. Navigates through key topics, including static and dynamic treatment regimes. Engages in discussions on sensitivity analysis, graphical models, identification algorithms, and the complex domain of causal discovery. Examines advanced techniques in causal inference, offering students an opportunity to apply theoretical principles to practical scenarios. Tackles challenging aspects such as time-varying exposures and sophisticated modeling techniques in the pursuit of accurate and meaningful outcomes.