Course Detail
Units:
3.0
Course Components:
Lecture
Description
This course will provide an overview of modern causal inference using the framework of counterfactual outcomes to provide rigorous definitions of causal effects, confounding and selection biases. Topics include the development of directed acyclic graphs to characterize patterns of confounding and define assumptions required for causal inferences, adjustment for confounding using propensity scores and doubly robust estimation, instrumental variables, as well as methods for controlling for time-dependent confounding including applications of inverse probability weighting under marginal structural models, G-estimation, and nested structural models. Methods for implementing methods for causal inference with standard statistical software will be emphasized. A theoretical supplement to this course will provide a more in-depth theoretical background.