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Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
Author(s) -
Lunceford Jared K.,
Davidian Marie
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1903
Subject(s) - propensity score matching , covariate , observational study , inverse probability weighting , confounding , inverse probability , weighting , econometrics , quantile , statistics , average treatment effect , estimation , causal inference , marginal structural model , computer science , mathematics , medicine , bayesian probability , posterior probability , economics , management , radiology
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.