The goal of many epidemiologic studies is to quantify the causal effect of an 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. These (causal) methods can be used under less restrictive conditions than traditional statistical methods.
For example, causal methods allow one to estimate the causal effect of a time-varying exposure in the presence of time-dependent confounders that lie on the causal pathway between exposure and outcome. 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. The course presents the latest methodologic developments for the design and analysis of longitudinal studies. 15 hrs.
- Recognize and formulate well defined questions concerning causal effects
- Use causal diagrams to represent a priori subject-matter knowledge and assumptions
- Identify the settings in which conventional methods for data analysis are inadequate