Buy this book
"Propensity score matching is a popular way to make causal inferences about a binary treatment in observational data. The validity of these methods depends on which variables are used to predict the propensity score. We ask: "Absent strong ignorability, what would be the effect of including an instrumental variable in the predictor set of a propensity score matching estimator?" In the case of linear adjustment, using an instrumental variable as a predictor variable for the propensity score yields greater inconsistency than the naive estimator. This additional inconsistency is increasing in the predictive power of the instrument. In the case of stratification, with a strong instrument, propensity score matching yields greater inconsistency than the naive estimator. Since the propensity score matching estimator with the instrument in the predictor set is both more biased and more variable than the naive estimator, it is conceivable that the confidence intervals for the matching estimator would have greater coverage rates. In a Monte Carlo simulation, we show that this need not be the case. Our results are further illustrated with two empirical examples: one, the Tennessee STAR experiment, with a strong instrument and the other, the Connors' (1996) Swan-Ganz catheterization dataset, with a weak instrument"--National Bureau of Economic Research web site.
Buy this book
Showing 1 featured edition. View all 1 editions?
Edition | Availability |
---|---|
1
Do instrumental variables belong in propensity scores?
2007, National Bureau of Economic Research
electronic resource /
in English
|
aaaa
|
Book Details
Edition Notes
Title from PDF file as viewed on 9/21/2007.
Includes bibliographical references.
Also available in print.
System requirements: Adobe Acrobat Reader.
Mode of access: World Wide Web.
Classifications
External Links
The Physical Object
ID Numbers
Community Reviews (0)
Feedback?History
- Created December 19, 2020
- 1 revision
Wikipedia citation
×CloseCopy and paste this code into your Wikipedia page. Need help?
December 19, 2020 | Created by MARC Bot | Imported from Library of Congress MARC record |