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Abstract Details
Activity Number:
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569
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #301316 |
Title:
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An Information Criterion for Sensitivity Analyses of the Treatment Ignorability Assumption
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Author(s):
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Chen-Pin Wang*+
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Companies:
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UTHSCSA
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Address:
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, , TX, 78229,
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Keywords:
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causal modeling ;
treatment ignorability ;
information criterion ;
Kullback-Leibler Distance
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Abstract:
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The treatment ignorability assumption (Rubin 1978) is critical in causal modeling. This paper proposes a Kullback-Leibler Distance based information criterion (KLD-IC) suitable for conducting sensitivity analyses for verifying the treatment ignorability assumption in the causal modeling framework. Here we focus on how the inference of certain statistic $T_{n}$ is affected by the violation of the treatment ignorability assumption. Stemmed from Goutis and Robert (1998), our proposed KLD-IC is the posterior mean of the KLD between the (predictive) distribution of a statistic $T_{n}$, under two likelihoods $r$ and $f$, where $r$ is the likelihood when the ignorability assumption is met and $f$ is the the likelihood when the ignorability assumption is violated. We examine the asymptotic properties of this KLD-IC under certain regularity conditions. We also show the impact of this asymptotic property for examples where only part of the regularity conditions are satisfied. We applied this KLD-IC to a study of pain among those with opioid dependence.
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Authors who are presenting talks have a * after their name.
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