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Abstract Details

Activity Number: 526
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #306098
Title: A Forest Approach to Defining a Study Population
Author(s): Justin Bleich*+ and Emil Pitkin
Companies: The Wharton School and The Wharton School
Address: 9 Winchester Drive, Howell, NJ, 07731, United States
Keywords: Observational Study ; Propensity Score ; Overlap ; Forest ; Classification

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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