Dealing with limited overlap in estimation of average treatment effects

Published

Journal Article

Estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions between treatment groups. 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 ad hoc methods for trimming the sample. We develop a systematic approach to addressing lack of overlap. We characterize optimal subsamples for which the average treatment effect can be estimated most precisely. 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 rule of thumb to discard all units with estimated propensity scores outside the range 0.1,0.9. © 2009 Biometrika Trust.

Full Text

Duke Authors

Cited Authors

  • Crump, RK; Hotz, VJ; Imbens, GW; Mitnik, OA

Published Date

  • March 1, 2009

Published In

Volume / Issue

  • 96 / 1

Start / End Page

  • 187 - 199

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asn055

Citation Source

  • Scopus