The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Optimal Matching for Observational Studies
Abstract
Matching is a common method of adjustment in observational studies. Currently, matched samples are constructed using greedy heuristics (or “stepwise” procedures) that produce, in general, suboptimal matchings. With respect to a particular criterion, a matched sample is suboptimal if it could be improved by changing the controls assigned to specific treated units, that is, if it could be improved with the data at hand. Here, optimal matched samples are obtained using network flow theory. In addition to providing optimal matched-pair samples, this approach yields optimal constructions for several statistical matching problems that have not been studied previously, including the construction of matched samples with multiple controls, with a variable number of controls, and the construction of balanced matched samples that combine features of pair matching and frequency matching. Computational efficiency is discussed. Extensive use is made of ideas from two essentially disjoint literatures, namely statistical matching in observational studies and graph algorithms for matching. The article contains brief reviews of both topics.
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Citations
Cites
The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800020109 · Cited by 13,120
Matching to remove bias in observational studies
Rubin, D. B. (1973) · Biometrics · doi:10.2307/2529684 · Cited by 737
Cited by
Recent Developments in the Econometrics of Program Evaluation
Imbens, G. W., & Wooldridge, J. M. (2009) · Journal of Economic Literature · doi:10.1257/jel.47.1.5 · Cited by 4,926
Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review
Imbens, G. W. (2004) · Review of Economics and Statistics · doi:10.1162/003465304323023651 · Cited by 2,994
Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation
Rubin, D. B. (2001) · Health Services and Outcomes Research Methodology · doi:10.1023/a:1020363010465 · Cited by 2,228
Dealing with limited overlap in estimation of average treatment effects
Crump, R. K., Hotz, V. J., Imbens, G. W., & Mitnik, O. A. (2009) · Biometrika · doi:10.1093/biomet/asn055 · Cited by 1,007
Comparison of Multivariate Matching Methods: Structures, Distances, and Algorithms
Gu, X. S., & Rosenbaum, P. R. (1993) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1993.10474623 · Cited by 702
Self-citation
Metadata
- Authors
- Rosenbaum, P. R.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1989-12
- Volume
- 84
- Issue
- 408
- Pages
- 1024-1032
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1972910909
- Cited by
- 715
- References
- 2
- FWCI
- 1.84
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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