140.666.11 THE USE OF CAUSAL DIAGRAMS IN EPIDEMIOLOGY
Provides a non-technical overview of causal DAGs theory, its relation to counterfactual theory, and its applications to causal inference. Describe how causal DAGs can be used to propose a systematic classification of biases in observational and randomized studies. Presents practical applications of causal DAGs theory to examples taken from various research areas in epidemiology, including cancer, pregnancy outcomes, and HIV/AIDS. Also describes the bias induced by the use of conventional statistical methods for the analysis of longitudinal studies with time-varying exposures.
Upon successful completion of this course, students will: 1) Understand causal problems as potential interventions, through the framework of potential outcomes and assignment mechanisms; 2) Understand the general role of designs and of different modes of statistical inference; 3) Identify the role of such inferences in studies with experimental treatment assignment; 4) Understand ignorable assignment of treatments, and learn ways to estimate their causal effects; understand the role of outcome models and of propensity score models; 5) Understand which observational studies can be viewed as having sequentially ignorable assignment of treatments, and learn ways to estimate their causal effects; 6) Be introduced to studies with noncompliance to treatment, and the method of instrumental variables for estimating causal effects; 7) Be introduced to more general examples of studies where the treatments can be viewed as only partially controlled; understand the implications on what are meaningful treatment effects, on design and estimation.
- Monday 8:30 - 5:00
Previous courses in introductory statistical methods.