The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Substantial Gains in Bias Reduction from Matching with a Variable Number of Controls
Open accessAbstract
Summary.In observational studies that match several controls to each treated subject, substantially greater bias reduction is possible if the number of controls is not fixed but rather is allowed to vary from one matched set to another. In certain cases, matching with a fixed number of controls may remove only 50% of the bias in a covariate, whereas matching with a variable number of controls may remove 90% of the bias, even though both control groups have the same number of controls in total. An example of matching in a study of surgical mortality is discussed in detail.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Self-citationRosenbaum, P. R., & Rubin, D. B. (1985) · The American Statistician · doi:10.2307/2683903 · Cited by 4,774
Self-citationInvited Commentary: Propensity Scores
Joffe, M. M., & Rosenbaum, P. R. (1999) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a010011 · Cited by 1,048
Self-citationControlling Bias in Observational Studies: A Review
Cochran, W. G. (2006) · Matched Sampling for Causal Effects · doi:10.1017/cbo9780511810725.005 · Cited by 958
Silber, J. H., Williams, S. V., Krakauer, H., & Schwartz, S. (1992) · Medical Care · doi:10.1097/00005650-199207000-00004 · Cited by 952
Matching to remove bias in observational studies
Rubin, D. B. (1973) · Biometrics · doi:10.2307/2529684 · Cited by 737
Optimal Matching for Observational Studies
Rosenbaum, P. R. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478868 · Cited by 715
Self-citationComparison 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-citationMatching to Remove Bias in Observational Studies
(2006) · Matched Sampling for Causal Effects · doi:10.1017/cbo9780511810725.007 · Cited by 600
The Planning of Observational Studies of Human Populations
Cochran, W. G., & Chambers, S. P. (1965) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2344179 · Cited by 564
Cited by
Austin, P. C. (2011) · Multivariate Behavioral Research · doi:10.1080/00273171.2011.568786 · Cited by 12,242
Austin, P. C. (2014) · Statistics in Medicine · doi:10.1002/sim.5984 · Cited by 1,422
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
Ho, D., Imai, K., King, G., Stuart, E., & Greifer, N. (2005) · CRAN: Contributed Packages · doi:10.32614/cran.package.matchit · Cited by 1,206
A comparison of 12 algorithms for matching on the propensity score
Austin, P. C. (2014) · Statistics in Medicine · doi:10.1002/sim.6004 · Cited by 1,085
Zhan, C. (2003) · JAMA · doi:10.1001/jama.290.14.1868 · Cited by 1,035
Metadata
- Authors
- Ming, K., & Rosenbaum, P. R.
- Venue
- Biometrics
- Publisher
- Oxford University Press (OUP)
- Published
- 2000-03
- Volume
- 56
- Issue
- 1
- Pages
- 118-124
- ISSN
- 0006-341X (ISSN-L)
- 1541-0420
- OpenAlex
- W2018270810
- Cited by
- 258
- References
- 24
- FWCI
- 3.75
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Substantial Gains in Bias Reduction from Matching with a Variable Number of Controls. https://app.cochat.ai/papers/10.1111/j.0006-341x.2000.00118.x
- The paper
- Ming, K., & Rosenbaum, P. R. (2000). Substantial Gains in Bias Reduction from Matching with a Variable Number of Controls. Biometrics. https://doi.org/10.1111/j.0006-341x.2000.00118.x
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
