The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
6. Matching with Multiple Controls to Estimate Treatment Effects in Observational Studies
Abstract
Matching to control for covariates in the estimation of treatment effects is not common in sociology, where multivariate data are most often analyzed using multiple regression and its generalizations. Matching can be a useful way to estimate these effects, especially when the treatment condition is comparatively rare in a population, and controls are numerous but mostly unlike the treatment cases. Matching on numerous covariates is abetted by the estimation of propensity scores, or functions of the probability that cases are treatments rather than controls. This procedure is illustrated in the estimation of the effects of an organizational innovation on Medicare mortality within hospitals; the data set is very large, but innovative hospitals few, and many of the remaining hospitals are quite unlike the hospitals constituting the treatment sub-sample. Results are based on a variance-components model that is extended to consider the effects of an additional covariate. They show effects of the organizational innovation comparable to those estimated via multiple regression models but with substantially reduced standard errors.
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Cites
Statistics and Causal Inference
Holland, P. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478354 · Cited by 5,293
Searle, S. R., Casella, G., & McCulloch, C. E. (1992) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316856 · Cited by 1,926
Matching to remove bias in observational studies
Rubin, D. B. (1973) · Biometrics · doi:10.2307/2529684 · Cited by 737
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
Hospital Characteristics and Mortality Rates
Hartz, A. J., Krakauer, H., Kuhn, E. M., Young, M., Jacobsen, S. J., Gay, G., Muenz, L., Katzoff, M., Bailey, R. C., & Rimm, A. A. (1989) · New England Journal of Medicine · doi:10.1056/nejm198912213212506 · Cited by 513
Cited by
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
Matching Methods for Causal Inference: A Review and a Look Forward
Stuart, E. A. (2010) · Statistical Science · doi:10.1214/09-sts313 · Cited by 5,478
THE ESTIMATION OF CAUSAL EFFECTS FROM OBSERVATIONAL DATA
Winship, C., & Morgan, S. L. (1999) · Annual Review of Sociology · doi:10.1146/annurev.soc.25.1.659 · Cited by 890
Harder, V. S., Stuart, E. A., & Anthony, J. C. (2010) · Psychological Methods · doi:10.1037/a0019623 · Cited by 802
Little, R. J., & Rubin, D. B. (2000) · Annual Review of Public Health · doi:10.1146/annurev.publhealth.21.1.121 · Cited by 721
Optimal Full Matching and Related Designs via Network Flows
Hansen, B. B., & Klopfer, S. O. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x137047 · Cited by 703
Growing Existing Customers’ Revenue Streams through Customer Referral Programs
Garnefeld, I., Eggert, A., Helm, S. V., & Tax, S. S. (2013) · Journal of Marketing · doi:10.1509/jm.11.0423 · Cited by 171
Transitions in romantic relationships and development of self-esteem.
Luciano, E. C., & Orth, U. (2017) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000109 · Cited by 149
Metadata
- Authors
- Smith, H. L.
- Venue
- Sociological Methodology
- Publisher
- SAGE Publications
- Published
- 1997-08
- Volume
- 27
- Issue
- 1
- Pages
- 325-353
- ISSN
- 0081-1750 (ISSN-L)
- 1467-9531
- OpenAlex
- W2083077778
- Cited by
- 368
- References
- 5
- FWCI
- 5.56
- Type
- journal article
- Language
- en
- Open access
- closed
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