Bias-Corrected Matching Estimators for Average Treatment Effects
Abstract
In Abadie and Imbens (2006), it was shown that simple nearest-neighbor matching estimators include a conditional bias term that converges to zero at a rate that may be slower than N1/2. As a result, matching estimators are not N1/2-consistent in general. In this article, we propose a bias correction that renders matching estimators N1/2-consistent and asymptotically normal. To demonstrate the methods proposed in this article, we apply them to the National Supported Work (NSW) data, originally analyzed in Lalonde (1986). We also carry out a small simulation study based on the NSW example. In this simulation study, a simple implementation of the bias-corrected matching estimator performs well compared to both simple matching estimators and to regression estimators in terms of bias, root-mean-squared-error, and coverage rates. Software to compute the estimators proposed in this article is available on the authors’ web pages (http://www.economics.harvard.edu/faculty/imbens/software.html) and documented in Abadie et al. (2003).
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When one models the outcome evolution of untreated units using a nearest-neighbor approach with a fixed number of matches, the resulting estimator is no longer asymptotically linear and standard bootstrap procedures are not asymptotically valid.
“Inference is more complicated, however, when one models the outcome evolution of untreated units using a nearestneighbor approach with a fixed number of matches: the resulting estimator is no longer asymptotically linear and thus standard bootstrap procedures are not asymptotically valid (e.g., Abadie and Imbens, 2006, 2008, 2011, 2012).”
From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Abadie and Imbens, 2011
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- Authors
- Abadie, A., & Imbens, G. W.
- Affiliations
- Harvard University (US)
- ORCID
- Imbens, Guido W.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 2011-01
- Volume
- 29
- Issue
- 1
- Pages
- 1-11
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W2094368311
- Cited by
- 1,416
- References
- 30
- Citations by year
- 2026: 61 · 2025: 97 · 2024: 91 · 2023: 107 · 2022: 112 · 2021: 120 · 2020: 113 · 2019: 111 · 2018: 114 · 2017: 101 · 2016: 87 · 2015: 90 · 2014: 57 · 2013: 50 · 2012: 46
- FWCI
- 36.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Business and Economic Statistics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation
- 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
- matching estimators, average treatment effect, bias correction, nearest neighbor matching, conditional bias, root-n consistency, asymptotic normality, regression estimator, causal inference
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