An introduction to the basic components of a directed acyclic graph (DAGs), how to identify structural sources of bias (i.e., confounding, selection bias, information bias) using a DAG, and extensions to time-varying treatments.
Standardization for time-fixed treatments is described, with a focus on (1) how to interpret standardized estimates, (2) how to use models to estimate standardized estimates, and (3) the use of bootstrapping to obtain 95% confidence intervals.
Instrumental variable (IV) estimation for time-fixed treatments, with an emphasis on the underlying assumptions of IV, common estimators for IV, and implementation of the methods in R.
Structures and mechanisms of different types of measurement bias.
An introduction to formulating causal questions for time-varying treatments, depicting time-varying treatments on a directed acyclic graph and understanding why conventional methods fail.
Extensions of inverse probability weighting to time-varying treatment strategies.