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Abstract Details
Activity Number:
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524
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract - #301583 |
Title:
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A Data-Adaptive Approach for Modeling Propensity Scores
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Author(s):
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Yeying Zhu*+ and Debashis Ghosh
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Companies:
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Penn State University and Penn State University
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Address:
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Department of Statistics, University Park, PA, 16803,
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Keywords:
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Causal inference ;
Logistic regression ;
Random forests ;
Two-stage modeling ;
Bias-variance ;
Observational data
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Abstract:
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In non-randomized observational studies, estimated differences between treatment groups may arise not only due to the treatment but also because of the masking effect of confounders. To adjust for confounding due to measured covariates, the average treatment effect is often estimated conditioning on propensity scores. In the literature, propensity scores are usually estimated by logistic regression. Alternatively, one can employ non-parametric classication algorithms, such as various tree-based methods or support vector machines. In this talk, we explore the effect of classification algorithms used to model propensity scores using ideas of bias and variance. In addition, we explore ways to combine logistic regression with nonparametric approaches to estimate propensity scores. Simulation studies are used to assess the performance of the newly proposed method and a data analysis example is presented in the end.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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