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Abstract Details
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Activity Number:
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526
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #306098 |
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Title:
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A Forest Approach to Defining a Study Population
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Author(s):
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Justin Bleich*+ and Emil Pitkin
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Companies:
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The Wharton School and The Wharton School
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Address:
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9 Winchester Drive, Howell, NJ, 07731, United States
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Keywords:
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Observational Study ;
Propensity Score ;
Overlap ;
Forest ;
Classification
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Abstract:
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Following the seminal work of Rosenbaum and Rubin (1983), matching based on the propensity score has become the predominant matching technique in observational studies. In order to reduce the asymptotic variance of the estimated average treatment effect (ATE), overlap between the propensity score distributions of the treatment and control groups is desirable. Tree-based approaches describe the overlapping populations in terms of their covariates, rather than propensity scores. We employ a novel method to define a study population that is as close to optimal as possible, which relies on a bootstrapping approach to search through a forest of prospective trees. Applications to real data are presented.
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