The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
Abstract
The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Previous theoretical arguments have shown that subclassification on the propensity score will balance all observed covariates. Subclassification on an estimated propensity score is illustrated, using observational data on treatments for coronary artery disease. Five subclasses defined by the estimated propensity score are constructed that balance 74 covariates, and thereby provide estimates of treatment effects using direct adjustment. These subclasses are applied within sub-populations, and model-based adjustments are then used to provide estimates of treatment effects within these sub-populations. Two appendixes address theoretical issues related to the application: the effectiveness of subclassification on the propensity score in removing bias, and balancing properties of propensity scores with incomplete data.
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In the citing paper
“Model selection is conducted through an iterative process, with the primary goal of achieving covariate balance afterwards (Heckman, Ichimura, Smith, & Todd, 1998; Rosenbaum & Rubin, 1984), as opposed to explicit modeling of the selection process.”
From Differential Effects of Adult Court Transfer on Juvenile Offender Recidivism (2010) · cites this paper as Rosenbaum & Rubin, 1984
In the citing paper
“Rosenbaum and Rubin (1984) contend that stratification using quintiles (i.e., five equally sized subgroups) can remove approximately 90% of the initial imbalance in each of the baseline covariates.”
From Differential Effects of Adult Court Transfer on Juvenile Offender Recidivism (2010) · cites this paper as Rosenbaum and Rubin (1984)
Citations
Cites
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The central role of the propensity score in observational studies for causal effects
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Self-citationDavid, F. N., & Tukey, J. W. (1977) · Biometrics · doi:10.2307/2529486 · Cited by 12,707
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Self-citationConditional Independence in Statistical Theory
Dawid, A. P. (1979) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1979.tb01052.x · Cited by 1,507
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Self-citationThe NEW ENGLAND JOURNAL of MEDICINE
Gelijns, A. C., Rosenberg, N., & Moskowitz, A. J. (2009) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789814273596_0013 · Cited by 1,000
Flagged first, then by citations
Research works printed but not linked: 3
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- Cox D. R., 1970, The Analysis of Binary Data
- Rubin D. B., 1970, “The Use of Matched Sampling and Regression Adjustment in Observational Studies
Cited by
Sorted by citations
Austin, P. C. (2011) · Multivariate Behavioral Research · doi:10.1080/00273171.2011.568786 · Cited by 12,242
Austin, P. C. (2009) · Statistics in Medicine · doi:10.1002/sim.3697 · Cited by 6,758
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
Statistics and Causal Inference
Holland, P. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478354 · Cited by 5,293
Metadata
- Authors
- Rosenbaum, P. R., & Rubin, D. B.
- Affiliations
- Educational Testing Service (US)
- University of Chicago (US)
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1984-09
- Volume
- 79
- Issue
- 387
- Pages
- 516-524
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W4239728164
- Cited by
- 3,082
- References
- 15
- Citations by year
- 2026: 100 · 2025: 128 · 2024: 140 · 2023: 136 · 2022: 142 · 2021: 175 · 2020: 154 · 2019: 147 · 2018: 164 · 2017: 147 · 2016: 169 · 2015: 133 · 2014: 178 · 2013: 143 · 2012: 140
- FWCI
- 5.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- propensity score, subclassification, observational studies, bias mitigation, covariate balance, treatment effect estimation, coronary artery disease, missing data
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