Abstract Details
Activity Number:
|
510
|
Type:
|
Invited
|
Date/Time:
|
Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Section on Nonparametric Statistics
|
Abstract - #307239 |
Title:
|
Addressing Missing Outcome Data in Randomized Experiments: A Design-Based Approach
|
Author(s):
|
Donald P. Green*+ and Holger L. Kern and Peter M. Aronow
|
Companies:
|
Columbia University and University of South Carolina and Yale University
|
Keywords:
|
experiments ;
causal inference ;
missing data ;
Manski bounds
|
Abstract:
|
Missing outcome data plague many randomized experiments. Common solutions such as selection models and multiple imputation rest on untestable assumptions that are not al- ways credible. In contrast, we propose a design-based approach for dealing with missing outcome data that makes minimal assumptions but still yields informative bounds on the average treatment effect. Our approach is based on a combination of the double sampling design (Neyman 1923; Hansen and Hurwitz 1946) and non-parametric worst case bounds (Birnbaum and Sirken 1950a,b; Manski 1990). We also derive the ex ante optimal exper- imental design when experimenters expect some fraction of outcome data to be missing. We illustrate the value of our approach through Monte Carlo simulations and the analysis of data from a randomized field experiment in Uganda.
|
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.