JSM 2004 Online Program

Abstract #301436

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Activity Number:	212
Type:	Topic Contributed
Date/Time:	Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #301436
Title:	Bayesian Nonparametrics and Causal Inference
Author(s):	Jennifer Hill*+ and Robert E. McCulloch
Companies:	Columbia University and University of Chicago
Address:	SIPA, 420 W. 118th St., 740 IAB, New York, NY, 10027,
Keywords:	Bayesian ; nonparametrics ; propensity scores ; causal inference
Abstract:	Propensity score approaches are becoming more popular for causal estimation in observational studies. Traditional estimation methods (logistic and probit regression), however, may not represent the best models for estimating propensity scores. In addition, no method for reliably estimating standard errors from resulting treatment effect estimates has been identified. A Bayesian nonparametric approach to propensity score estimation that addresses both of these issues will be illustrated.

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