Machine Learning for Propensity Score Estimation: A Systematic Review and Reporting Guidelines

Machine learning has become a common approach for estimating propensity scores for quasi-experimental research using matching, weighting, or stratification on the propensity score. This systematic review examined machine learning applications for propensity score 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 (CART), neural networks, and the super learner were also used in more than five percent of studies. The most frequently used packages to estimate propensity scores were twang, gbm and randomforest in the R statistical software. The review identified many hyperparameter configurations used for machine learning methods. However, it also shows that hyperparameters are frequently under-reported, as well as critical steps of the propensity score analysis, such as the covariate balance evaluation. A set of guidelines for reporting the use of machine learning for propensity score estimation is provided.