Model Averaging in Causal Inference

• Activity Number: 325
• Type: Topic Contributed
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Matthew Cefalu*+ and Francesca Dominici and Giovanni Parmigiani
• Companies: Harvard School of Public Health and HSPH and Dana-Farber Cancer Institute
• Keywords: Model uncertainty ; Confounder selection ; Bayesian model averaging ; Causal inference ; Propensity score

Abstract

In the age of big data, where large and complex datasets are used to estimate causal effects, researchers are increasingly being challenged with decisions on how to best control for a high-dimensional set of potential confounders. Typically, a single propensity score model is used in some form to adjust for confounding, while the uncertainty surrounding the procedure to arrive at this propensity score model is often ignored. We propose a general Bayesian causal framework that overcomes the limitations described above through the use of Bayesian model averaging. We illustrate the proposed framework by applying it in the context of propensity score matching and double robust estimation.