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Review and comparison of treatment effect estimators using propensity and prognostic scores

Author

Listed:
  • Lee Myoung-Jae

    (Department of Economics, Korea University, Seoul 02841, Korea)

  • Lee Sanghyeok

    (Department of Economics, American University in Cairo, New Cairo 11835, Egypt)

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.

Suggested Citation

  • Lee Myoung-Jae & Lee Sanghyeok, 2022. "Review and comparison of treatment effect estimators using propensity and prognostic scores," The International Journal of Biostatistics, De Gruyter, vol. 18(2), pages 357-380, November.
    • Handle: RePEc:bpj:ijbist:v:18:y:2022:i:2:p:357-380:n:6
    DOI: 10.1515/ijb-2021-0005
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