Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence
Abstract
We compare propensity-score matching methods with covariatematching estimators. We first discuss the data requirements of propensity-score matching estimators and covariate matching estimators. Then we propose two new matching metrics incorporating the treatment outcome information and participation indicator information, and discuss the motivations of different metrics. Next we study the small-sample properties of propensity-score matching versus covariate matching estimators, and of different matching metrics, through Monte Carlo experiments. Through a series of simulations, we provide some guidance to practitioners on how to choose among different matching estimators and matching metrics.
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When sample sizes are small, propensity score matching performs poorly compared with other matching estimators.
“Zhao (2004) shows that when sample sizes are small, as they are in the Perry data, propensity score matching performs poorly when compared with other matching estimators.”
From Analyzing social experiments as implemented: A reexamination of the evidence from the HighScope Perry Preschool Program (2010) · cites this paper as Zhao (2004)
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SOME PRACTICAL GUIDANCE FOR THE IMPLEMENTATION OF PROPENSITY SCORE MATCHING
Caliendo, M., & Kopeinig, S. (2008) · Journal of Economic Surveys · doi:10.1111/j.1467-6419.2007.00527.x · Cited by 6,238 · see citing works
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Matching Methods for Causal Inference: A Review and a Look Forward
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Does matching overcome lalonde’s critique of nonexperimental estimators?
A. Smith, J., & E. Todd, P. (2005) · Journal of Econometrics · doi:10.1016/j.jeconom.2004.04.011 · Cited by 3,176 · see citing works
Direct versus Indirect Colonial Rule in India: Long-Term Consequences
Iyer, L. (2010) · Review of Economics and Statistics · doi:10.1162/rest_a_00023 · Cited by 344 · see citing works
(2010) · Quantitative Economics · doi:10.3982/qe8 · Cited by 304 · see citing works
“23 Zhao (2004) shows that when sample sizes are small, as they are in the Perry data, propensity score matching performs poorly when compared with other matching estimators.”
- Authors
- Zhao, Z.
- Affiliations
- Peking University (CN)
- Corresponding author
- Zhao, Zhong
- ORCID
- Zhao, Zhong
- Venue
- Review of Economics and Statistics
- Publisher
- MIT Press - Journals
- Published
- 2004-02
- Volume
- 86
- Issue
- 1
- Pages
- 91-107
- ISSN
- 0034-6535 (ISSN-L)
- 1530-9142
- OpenAlex
- W1980072647
- Cited by
- 389
- References
- 4
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 20 · 2023: 17 · 2022: 20 · 2021: 17 · 2020: 12 · 2019: 14 · 2018: 17 · 2017: 12 · 2016: 14 · 2015: 29 · 2014: 24 · 2013: 24 · 2012: 24
- FWCI
- 15.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Review of Economics and Statistics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Education › School Choice and Performance
- Keywords
- propensity score matching, covariate matching, treatment effect estimation, data requirements, small sample properties, treatment participation
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