Machine learning for propensity score estimation: A systematic review and reporting guidelines

Machine learning (ML) has become a common approach for estimating propensity scores (PSs) for quasi-experimental research using matching, weighting, or stratification on the PS. This systematic review examined 179 applications of ML for PS estimation across different fields, such as health, education, social sciences, and business over 40 years. The results show that the gradient boosting machine (GBM) is the most frequently used method, followed by random forest. Classification and regression trees, neural networks, and the super learner were also used in more than 5% of studies. The most frequently used packages to estimate PSs were twang, gbm, and randomforest in the R statistical software. The review identified that critical steps of the propensity score analysis are frequently underreported. Covariate balance evaluation was not reported by 48.04% of articles. Also, improper use of p values for covariate balance evaluation was identified in 13.97% of the studies. Only 22.8% of studies performed a sensitivity analysis. Many hyperparameter configurations were used for ML methods, but only 46.9% of studies reported the hyperparameters used. A set of guidelines for reporting the use of ML for PS estimation is provided. (PsycInfo Database Record (c) 2026 APA, all rights reserved).