Department of Biostatistics Seminar/Workshop Series

Causal Estimation Using Instrumental Variables in Observational and Randomized Studies

Tood A. MacKenzie, Ph.D., Professor, Department of Biomedical Data Science, Dartmouth

Estimation of causal effects is a primary goal of statistics. Estimates from observational studies are subject to selection bias, while estimates from randomized studies are subject to bias due to non-compliance. In observational studies confounding by unmeasured confounders cannot be overcome by regression adjustment, conditioning on propensity scores or inverse weighted propensities. The method of instrumental variables can overcome bias due to unmeasured confounding. In this talk I discuss estimation using instrumental variables in both observational and experimental studies, including the reporting of baseline characteristics for the patients to whom these estimators apply and its extension to dependent variables that are right censored.
Topic revision: r1 - 10 Jul 2017, AshleeBartley
 

This site is powered by FoswikiCopyright © 2013-2022 by the contributing authors. All material on this collaboration platform is the property of the contributing authors.
Ideas, requests, problems regarding Vanderbilt Biostatistics Wiki? Send feedback