Dealing with Limited Overlap in Estimation of Average Treatment Effects

Richard Crump, V. Joseph Hotz, Guido Imbens and Oscar Mitnik

No 716, Working Papers from University of Miami, Department of Economics

Abstract: Estimation of average treatment effects under unconfounded or ignorable 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 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].

Keywords: Average Treatment Effects; Causality; Unconfoundness; Overlap; Treatment Effect Heterogeneity (search for similar items in EconPapers)
JEL-codes: C14 C21 C52 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2004-07, Revised 2007-06-12
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

Forthcoming: Under Review

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Related works:
Journal Article: Dealing with limited overlap in estimation of average treatment effects (2009)
Working Paper: Dealing with Limited Overlap in Estimation of Average Treatment Effects (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:mia:wpaper:0716

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