Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
Top Cited Papers
- 24 August 2004
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 23 (19) , 2937-2960
- https://doi.org/10.1002/sim.1903
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.Keywords
This publication has 17 references indexed in Scilit:
- Economic Evaluation of Breast Cancer Treatment: Considering the Value of Patient ChoiceJournal of Clinical Oncology, 2003
- Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models: RejoinderJournal of the American Statistical Association, 1999
- On Estimating the Causal Effects of DNR OrdersMedical Care, 1999
- Estimating Causal Effects from Large Data Sets Using Propensity ScoresAnnals of Internal Medicine, 1997
- Estimation of Regression Coefficients When Some Regressors are not Always ObservedJournal of the American Statistical Association, 1994
- Model-Based Direct AdjustmentJournal of the American Statistical Association, 1987
- Reducing Bias in Observational Studies Using Subclassification on the Propensity ScoreJournal of the American Statistical Association, 1984
- The central role of the propensity score in observational studies for causal effectsBiometrika, 1983
- Estimating causal effects of treatments in randomized and nonrandomized studies.Journal of Educational Psychology, 1974
- A Generalization of Sampling Without Replacement from a Finite UniverseJournal of the American Statistical Association, 1952