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Research Article
Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
Article first published online: 24 AUG 2004
DOI: 10.1002/sim.1903
Copyright © 2004 John Wiley & Sons, Ltd.
1097-0258/asset/cover.gif?v=1&s=64ebf4a6597e744f418c952845cddf175ccc795f "Statistics in Medicine")
Additional Information
How to Cite
Lunceford, J. K. and Davidian, M. (2004), Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statist. Med., 23: 2937–2960. doi: 10.1002/sim.1903
Publication History
- Issue published online: 24 AUG 2004
- Article first published online: 24 AUG 2004
- Manuscript Accepted: APR 2004
- Manuscript Received: SEP 2003
Funded by
- NIH. Grant Numbers: R01-CA085848, R37-AI031789
- Abstract
- References
- Cited By
Keywords:
- covariate balance;
- double robustness;
- inverse-probability-of-treatment-weighted-estimator;
- observational data
Abstract
Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting for confounding: methods based on stratification of observations by quantiles of estimated propensity scores and methods based on weighting observations by the inverse of estimated propensity scores. We review popular versions of these approaches and related methods offering improved precision, describe theoretical properties and highlight their implications for practice, and present extensive comparisons of performance that provide guidance for practical use. Copyright © 2004 John Wiley & Sons, Ltd.