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Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
Paul R. Rosenbaum and Donald B. Rubin
Journal of the American Statistical Association
Vol. 79, No. 387 (Sep., 1984), pp. 516-524
Stable URL: http://www.jstor.org/stable/2288398
Page Count: 9
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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 subpopulations, and model-based adjustments are then used to provide estimates of treatment effects within these subpopulations. 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.
Journal of the American Statistical Association © 1984 American Statistical Association