Abstract Details
Activity Number:
|
73
|
Type:
|
Topic Contributed
|
Date/Time:
|
Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
Abstract - #307782 |
Title:
|
Balancing Covariates via Propensity Score Weighting
|
Author(s):
|
Fan Li*+ and Alan Zaslavsky and Kari Lock Morgan
|
Companies:
|
Duke University and Harvard University and Duke University
|
Keywords:
|
balance ;
causal inference ;
confounding ;
overlap ;
propensity score ;
weighting
|
Abstract:
|
Balance in the covariate distributions is crucial for an unconfounded descriptive or causal comparison between different groups. However, lack of overlap in the covariates is common in observational studies. This article focuses on weighting strategies for balancing covariates. A general class of weights --- the balancing weights --- that balance the expectation of the covariates in the treatment and the control groups is proposed. The framework is closely related to propensity score and includes several existing weights, such as the Horvitz-Thompson weight, as special cases. In particular, we advocate a new type of weight --- the overlap weight --- that leads to a comparison for the subpopulation with the most overlap in the covariates between two groups. We show that the overlap weight minimizes the asymptotic variances of the weighted average treatment effect among the class of balancing weights. Simulated and real examples are presented to illustrate the method and compare with the existing approaches. Comparison to matching and subclassification methods is also discussed.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.