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Activity Number:
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568
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #304981 |
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Title:
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Extending Propensity Score Subclassification Approach for Causal Effect Estimation Allowing Covariate Measurement Error
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Author(s):
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Yi Huang*+ and Karen Bandeen-Roche
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Companies:
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University of Maryland, Baltimore County and Johns Hopkins Bloomberg School of Public Health
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Address:
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1000 Hilltop Circle, Baltimore, MD, 21250,
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Keywords:
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Causal Inference ; Finite Mixture Model ; Nondifferential Measurement Error ; Propensity Score Subclassification ; Balancing Criterion ; ACE Estimation
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Abstract:
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In many studies on average causal effect (ACE) evaluation, the underlying true covariates are measured with error, which is challenging for current propensity score framework. The naive approach is to ignore the error and use the observed covariates in current framework for ACE estimation. We extend the existing causal assumptions to incorporate errors-in-covariates and demonstrate that the naive approach typically produces biased ACE inference. We also propose a flexible finite mixture framework for ACE estimation reflecting a covariate-balancing criterion in a joint likelihood, which unifies subgroup membership assessment and subgroup-specific treatment effect evaluation. Its performance will be evaluated by multiple simulations studies. In all, this model is proposed to improve ACE estimation under errors-in-covariates situations based on propensity score framework.
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