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Abstract Details

Activity Number: 670
Type: Contributed
Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
Sponsor: Social Statistics Section
Abstract - #306843
Title: Using Simulated Data for Covariate Selection in Propensity Score Models
Author(s): Douglas Galagate*+
Companies: University of Maryland
Address: Mathematics Building , College Park, MD, 20742,
Keywords: simulation ; matching ; propensity score ; variable selection

The large amount of observational data available elicits many opportunities for statisticians to find evidence for trends or relationships. Matching methods are used to create groups of units that have similar characteristics with one group receiving a treatment while the other does not. This is done to mimic a randomized experiment with the goal of estimating a treatment effect. The main goal of this work is to improve upon current variable selection techniques and to find optimal strategies for matching methods when dealing with observational data. We will use simulated data, taking into account the complexities in real-life, to analyze variable selection strategies in propensity score models.

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