Abstract Details
Activity Number:
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330
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Type:
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Invited
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Date/Time:
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Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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SSC
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Abstract #314406
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View Presentation
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Title:
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Model Averaging for Causal Inference
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Author(s):
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Matthew Cefalu*
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Companies:
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RAND Corporation
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Keywords:
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Confounder selection ;
Variable selection ;
Causal inference ;
Model averaging
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Abstract:
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Data-driven variable selection methods are useful tools for causal inference. This talk will highlight the use of Bayesian model averaging for confounder selection and discuss several related approaches. A key feature of Bayesian model averaging is that it allows researchers to incorporate prior information into the analysis. This prior can include conditions needed to be satisfied for a covariate to be considered a confounder and any prior information on which covariates are more likely to be confounders. All uncertainty in the confounder selection process is fully accounted for in the final effect estimates.
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Authors who are presenting talks have a * after their name.
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