With Richard Guo, PhD
Assistant Professor of Statistics, University of Michigan
Confounder selection, namely choosing a set of covariates to control for confounding between a treatment and an outcome, is arguably the most important step in the design of observational studies. Previous methods, such as Pearl's back-door criterion, typically require pre-specifying a causal graph, which can often be difficult in practice. We propose an interactive procedure for confounder selection that does not require pre-specifying the graph or the set of observed variables. I will illustrate this procedure using the Shiny WebApp we developed, which elicits user input bit by bit until either a set of covariates are found to control for confounding or it can be determined that no such set exists.
The Department of Epidemiology & Biostatistics welcomes all participants to our events. If you need a reasonable accommodation to participate in this event because of a disability, please contact Liz Buggs ([email protected]).