The goal of many epidemiologic studies is to quantify the causal effect of a treatment (or exposure) on an outcome. In contrast, commonly used statistical methods provide measures of association that may lack a causal interpretation even when the investigator adjusts for all potential confounders in the analysis of a properly designed study.

To eliminate the discordance between the causal goals and the associational methods in epidemiology, it is necessary to a) formally define causal concepts such as causal effect and confounding, b) identify the conditions required to estimate causal effects, and c) use analytical methods that, under those conditions, provide estimates that can be endowed with a causal interpretation. This course combines counterfactual theory and graph theory to present an integrated framework for causal inference from observational data, with a special emphasis on complex longitudinal data. Specifically, the course will introduce g-methods (inverse probability weighting of marginal structural models; parametric g-formula; and g-estimation of structural nested models) in the setting of time-fixed treatments and demonstrate inverse probability weighting for addressing causal questions regarding sustained treatment strategies. On the final day, alternative or complementary approaches will be discussed (e.g., instrumental variable approaches; quantitative bias analysis).