Review and comparison of treatment effect estimators using propensity and prognostic scores
Second Author's Department
Economics Department
Document Type
Research Article
Publication Title
The international journal of biostatistics
Publication Date
11-1-2022
doi
10.1515/ijb-2021-0005
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.
First Page
357
Last Page
380
Recommended Citation
APA Citation
Lee, M.
&
Lee, S.
(2022). Review and comparison of treatment effect estimators using propensity and prognostic scores. The international journal of biostatistics, 18(2), 357–380.
10.1515/ijb-2021-0005
https://fount.aucegypt.edu/faculty_journal_articles/4677
MLA Citation
Lee, Myoung-Jae, et al.
"Review and comparison of treatment effect estimators using propensity and prognostic scores." The international journal of biostatistics, vol. 18,no. 2, 2022, pp. 357–380.
https://fount.aucegypt.edu/faculty_journal_articles/4677