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TESTING LIMITED OVERLAP

Published online by Cambridge University Press:  13 May 2024

Xinwei Ma Open the ORCID record for Xinwei Ma [Opens in a new window] ,
Yuya Sasaki Open the ORCID record for Yuya Sasaki [Opens in a new window]  and
Yulong Wang Open the ORCID record for Yulong Wang [Opens in a new window]
Xinwei Ma
Affiliation:
University of California, San Diego
Yuya Sasaki*
Affiliation:
Vanderbilt University
Yulong Wang
Affiliation:
Syracuse University
*
Address correspondence to Yuya Sasaki, Department of Economics, Vanderbilt University, 415 Calhoun Hall, Nashville, TN 37240, USA; e-mail: yuya.sasaki@vanderbilt.edu.
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Abstract

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Extreme propensity scores arise in observational studies when treated and control units have very different characteristics. This is commonly referred to as limited overlap. In this paper, we propose a formal statistical test that helps assess the degree of limited overlap. Rejecting the null hypothesis in our test indicates either no or very mild degree of limited overlap and hence reassures that standard treatment effect estimators will be well behaved. One distinguishing feature of our test is that it only requires the use of a few extreme propensity scores, which is in stark contrast to other methods that require consistent estimates of some tail index. Without the need to extrapolate using observations far away from the tail, our procedure is expected to exhibit excellent size properties, a result that is also borne out in our simulation study.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 41 , Issue 5 , October 2025 , pp. 1129 - 1162
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Footnotes

We would like to express our sincere gratitude to Peter C. B. Phillips and three anonymous reviewers for their invaluable comments and insights, which greatly enhanced the quality of this paper. Sasaki thanks Brian and Charlotte Grove, Chair for research support.

References

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