The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R
Open accessAbstract
Matching is an R package which provides functions for multivariate and propensity score matching and for finding optimal covariate balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance actually has been obtained are provided. The underlying matching algorithm is written in C++, makes extensive use of system BLAS and scales efficiently with dataset size. The genetic algorithm which finds optimal balance is parallelized and can make use of multiple CPUs or a cluster of computers. A large number of options are provided which control exactly how the matching is conducted and how balance is evaluated.
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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
Practical Nonparametric Statistics
Groggel, D. J. (2000) · Technometrics · doi:10.1080/00401706.2000.10486067 · Cited by 11,041
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436
Statistics and Causal Inference
Holland, P. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478354 · Cited by 5,293
Propensity Score-Matching Methods for Nonexperimental Causal Studies
Dehejia, R. H., & Wahba, S. (2002) · Review of Economics and Statistics · doi:10.1162/003465302317331982 · Cited by 4,846
Rosenbaum, P. R., & Rubin, D. B. (1985) · The American Statistician · doi:10.2307/2683903 · Cited by 4,774
A generalization of sampling without replacement from a finite universe
Horvitz, D. G., & Thompson, D. J. (1952) · Journal of the American Statistical Association · doi:10.1080/01621459.1952.10483446 · Cited by 4,293
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
Large sample properties of matching estimators for average treatment effects
Abadie, A., & Imbens, G. W. (2006) · Econometrica · doi:10.1111/j.1468-0262.2006.00655.x · Cited by 2,908
Estimating Causal Effects from Large Data Sets Using Propensity Scores
Rubin, D. B. (1997) · Annals of Internal Medicine · doi:10.7326/0003-4819-127-8_part_2-199710151-00064 · Cited by 2,877
Cited by
Austin, P. C. (2011) · Multivariate Behavioral Research · doi:10.1080/00273171.2011.568786 · Cited by 12,242
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
MatchIt : Nonparametric Preprocessing for Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011) · Journal of Statistical Software · doi:10.18637/jss.v042.i08 · Cited by 4,385
Environmental Policy and Directed Technological Change: Evidence from the European Carbon Market
Calel, R., & Dechezleprêtre, A. (2016) · Review of Economics and Statistics · doi:10.1162/rest_a_00470 · Cited by 1,147
Microbiome differential abundance methods produce different results across 38 datasets
Nearing, J. T., Douglas, G. M., Hayes, M. G., MacDonald, J., Desai, D. K., Allward, N., Jones, C. M. A., Wright, R. J., Dhanani, A. S., Comeau, A. M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28034-z · Cited by 992
Metadata
- Authors
- Sekhon, J. S.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2011
- Volume
- 42
- Issue
- 7
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W3122049309
- Cited by
- 1,543
- References
- 52
- FWCI
- 81.79
- Type
- journal article
- Language
- en
- Open access
- diamond
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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.
