B0541
Title: Flexible Bayesian models for causal inference and missing data
Authors: Jason Roy - Rutgers University (United States) [presenting]
Abstract: Bayesian methods have not been widely used for causal inference in observational studies. A possible reason for this is that causal inference in a likeihood-based framework often requires modeling the joint distirbution of all of the observed data, including covariates. However, recent developments in Bayesian nonparametric (BNP) modeling, along with increasing computing capacity, have opened the door to a new, potentially powerful approach to causal inference in a variety of settings. We develop a general joint Dirchlet process mixture model and show how it can be used to obtain posterior inference for any causal effect of interest. The extra effort needed to model a full observed data distribution has many potential benefits, including efficiency gains, full posterior inference rather than just point estimates and confidence intervals, automatic imputation of missing data, and a general way to account for uncertainty about a variety of assumptions. We compare our method with inverse probability of treatment-weighted estimators in simulation studies.
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