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Updated: Sep 2, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Review and comparison of treatment effect estimators using propensity and prognostic scores
Myoung-Jae Lee1, Sanghyeok Lee2
1Department of Economics, Korea University, Seoul 02841, Korea.
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.
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