TY  - JOUR
AU  - Crump,Richard K.
AU  - Hotz,V. Joseph
AU  - Imbens,Guido W.
AU  - Mitnik,Oscar A.
TI  - Moving the Goalposts: Addressing Limited Overlap in the Estimation of Average Treatment Effects by Changing the Estimand
JF  - National Bureau of Economic Research Technical Working Paper Series
VL  - No. 330
PY  - 2006
Y2  - October 2006
UR  - http://www.nber.org/papers/t0330
L1  - http://www.nber.org/papers/t0330.pdf
N1  - Author contact info:
Richard Crump
Capital Markets Function
Federal Reserve Bank of New York
33 Liberty Street
New York, NY 10045
E-Mail: richard.crump@ny.frb.org

V. Joseph Hotz
Department of Economics
Box 90097
Duke University
Durham, NC 27708-0097
Tel: 919-660-1841
Fax: 919-684-8974
E-Mail: hotz@econ.duke.edu

Guido Imbens
Graduate School of Business
Stanford University
655 Knight Way
Stanford, CA 94305
Tel: 617/384-7485
Fax: 617/495-7730
E-Mail: Imbens@stanford.edu

Oscar Mitnik
Department of Economics
University of Miami
P.O. Box 248126
Coral Gables, FL  33124-6550
E-Mail: omitnik@miami.edu
AB  - Estimation of average treatment effects under unconfoundedness or exogenous treatment assignment is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of specification. In such cases researchers have often used informal methods for trimming the sample. In this paper we develop a systematic approach to addressing such lack of overlap. We characterize optimal subsamples for which the average treatment effect can be estimated most precisely, as well as optimally weighted average treatment effects. Under some conditions the optimal selection rules depend solely on the propensity score. For a wide range of distributions a good approximation to the optimal rule is provided by the simple selection rule to drop all units with estimated propensity scores outside the range [0.1,0.9].
ER  -