Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails

Daniel Millimet and Rusty Tchernis

No 2008-008, CAEPR Working Papers from Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington

Abstract: We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case of selection on unobservables. We then propose a new minimum biased estimator of the average treatment effect. We assess the finite sample performance of our estimator using simulated data, as well as a timely application examining the causal effect of the School Breakfast Program on childhood obesity. We find our new estimator to be quite advantageous in many situations, even when selection is only on observables.

Keywords: Treatment Effects; Propensity Score; Bias; Unconfoundedness; Selection on Unobservables (search for similar items in EconPapers)
JEL-codes: C21 C52 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2008-04
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://caepr.indiana.edu/RePEc/inu/caeprp/caepr2008-008.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found

Related works:
Working Paper: Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails (2008)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inu:caeprp:2008008

Access Statistics for this paper

More papers in CAEPR Working Papers from Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington Contact information at EDIRC.
Bibliographic data for series maintained by Center for Applied Economics and Policy Research ().