The central role of the propensity score in observational studies for causal effects
The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.…
# The central role of the propensity score in observational studies for causal effects
> OpenAlex Metadata Hub · https://openalex.org/W2150291618
## Bibliographic
- **DOI:** 10.1093/biomet/70.1.41
- **Year:** 1983
- **Citations:** 31045
- **Open Access:** Yes (bronze)
- **License:** —
- **Source:** https://academic.oup.com/biomet/article-pdf/70/1/41/662954/70-1-41.pdf
## Authors
- Paul R. Rosenbaum
- Donald B. Rubin
## Abstract
The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates. Applications include: (i) matched sampling on the univariate propensity score, which is a generalization of discriminant matching, (ii) multivariate adjustment by subclassification on the propensity score where the same subclasses are used to estimate treatment effects for all outcome variables and in all subpopulations, and (iii) visual representation of multivariate covariance adjustment by a two- dimensional plot.
## Keywords
Propensity score matching, Mathematics, Covariate, Statistics, Univariate, Observational study, Multivariate statistics, Econometrics, Matching (statistics)
## Concepts
- Propensity score matching
- Mathematics
- Covariate
- Statistics
- Univariate
- Observational study
- Multivariate statistics
- Econometrics
- Matching (statistics)
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*Metadata only — full text not imported unless Open Access license permits.*
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Tóm lược học thuật (đã diễn giải): The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates. Applications include: (i) matched sampling on the univariate propensity score, which is a generalization of discriminant matching, (ii) multivariate adjustment by subclassification on the propensity score where the same subclasses are used to estimate treatment effects for all outcome variables and in all subpopulations, and (iii) visual representation of multivariate covariance adjustment by a two- dimensional plot.
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1. The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates.
2. Both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.
3. Applications include: (i) matched sampling on the univariate propensity score, which is a generalization of discriminant matching, (ii) multivariate adjustment by subclassification on the propensity score where the same subclasses are used to estimate treatment effects for all outcome variables and in all subpopulations, and (iii) visual representation of multivariate covariance adjustment by a two- dimensional plot.
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