This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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296
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #308299 |
Title:
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A Bayesian Semiparametric Approach to Causal Inference with Intermediate Variables
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Author(s):
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Fan Li*+ and Scott Schwartz and Fabrizia Mealli
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Companies:
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Duke University and Duke University and University of Florence
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Address:
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, Durham, 27708, USA
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Keywords:
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Bayesian nonparametrics ;
causal inference ;
Dirichlet process mixture ;
intermediate variables ;
principal stratification ;
compliance
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Abstract:
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In causal inference, treatment comparisons often need to adjust for post-treatment (intermediate) variables. Principal stratification (PS) is a popular framework to deal with such variables. Continuous intermediate variables introduce inferential challenges to the PS analysis. Existing methods usually rely on a fully parametric model for the joint distribution of the potential intermediate variables, which is often inadequate to capture complex density shapes. To provide the necessary flexibility in modeling, we propose a Bayesian semiparametric approach that combines a parametric model for the response variables with a Bayesian nonparametric model for the intermediate variables using the Dirichlet process mixture. The method is illustrated by two examples: one concerning partial compliance in randomized clinical trial, another concerning the lock-in effect in job training programs.
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