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 matchingMathematicsCovariateStatisticsUnivariateObservational studyMultivariate statisticsEconometricsMatching (statistics)
Affiliated Institutions
Related Publications
Why Propensity Scores Should Not Be Used for Matching
We show that propensity score matching (PSM), an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal—thus ...
On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects
In this paper, the role of the propensity score in the efficient estimation of average treatment effects is examined. Under the assumption that the treatment is ignorable given ...
A geometric approach to assess bias due to omitted covariates in generalized linear models
Misspecifying generalized linear models by omitting covariates associated with the response can result in seriously biased estimates of the effects of the included covariates. F...
A Geometric Approach to Assess Bias Due to Omitted Covariates in Generalized Linear Models
Misspecifying generalized linear models by omitting covariates associated with the response can result in seriously biased estimates of the effects of the included covariates. F...
Regression Splines in the Cox Model with Application to Covariate Effects in Liver Disease
Abstract The Cox proportional hazards model restricts the log hazard ratio to be linear in the covariates. A smooth nonlinear covariate effect may go undetected in this model bu...
Publication Info
- Year
- 1983
- Type
- article
- Volume
- 70
- Issue
- 1
- Pages
- 41-55
- Citations
- 29661
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
29661
OpenAlex
Cite This
Paul R. Rosenbaum,
Donald B. Rubin
(1983).
The central role of the propensity score in observational studies for causal effects.
Biometrika
, 70
(1)
, 41-55.
https://doi.org/10.1093/biomet/70.1.41
Identifiers
- DOI
- 10.1093/biomet/70.1.41