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- W4292454352 endingPage "380" @default.
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- W4292454352 abstract "In finding effects of a binary treatment, practitioners use mostly either propensity score matching (PSM) or inverse probability weighting (IPW). However, many new treatment effect estimators are available now using propensity score and prognostic score, and some of these estimators are much better than PSM and IPW in several aspects. In this paper, we review those recent treatment effect estimators to show how they are related to one another, and why they are better than PSM and IPW. We compare 26 estimators in total through extensive simulation and empirical studies. Based on these, we recommend recent treatment effect estimators using overlap weight, and targeted MLE using statistical/machine learning, as well as a simple regression imputation/adjustment estimator using linear prognostic score models." @default.
- W4292454352 created "2022-08-20" @default.
- W4292454352 creator A5043815153 @default.
- W4292454352 creator A5069130153 @default.
- W4292454352 date "2022-08-09" @default.
- W4292454352 modified "2023-09-26" @default.
- W4292454352 title "Review and comparison of treatment effect estimators using propensity and prognostic scores" @default.
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- W4292454352 doi "https://doi.org/10.1515/ijb-2021-0005" @default.
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