Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study.
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.
Duke Scholars
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Related Subject Headings
- Treatment Outcome
- Statistics & Probability
- Regression Analysis
- Monte Carlo Method
- Humans
- Data Interpretation, Statistical
- Computer Simulation
- 4905 Statistics
- 4202 Epidemiology
- 1117 Public Health and Health Services
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Treatment Outcome
- Statistics & Probability
- Regression Analysis
- Monte Carlo Method
- Humans
- Data Interpretation, Statistical
- Computer Simulation
- 4905 Statistics
- 4202 Epidemiology
- 1117 Public Health and Health Services