This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
|CE_01C||Sun, 8/1/2010, 8:30 AM - 5:00 PM||CC-11(East)|
|Causal Inference — Continuing Education Course|
|Instructor(s): Miguel Hernan, Harvard School of Public Health, James Robins, Harvard School of Public Health|
|This course introduces statistical methods for drawing causal inferences from observational studies. Informal epidemiologic concepts such as confounding and selection bias are formally defined within the context of both causal diagrams and a counterfactual causal model. Methods for the analysis of the causal effects of time-varying exposures in the presence of time dependent covariates that are simultaneously confounders and intermediate variables are emphasized. These methods include inverse probability weighting of marginal structural models, g-estimation of structural nested models, and the parametric g-formula. Learning Objectives; 1) Recognize and formulate well defined questions concerning causal effects, 2) Learn about several modeling approaches to estimate causal effects, and 3) Identify the relative advantages and disadvantages of each modeling approach.|